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159                publications
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193      <div class="post">
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195  <header class="post-header">
196    <h1 class="post-title">
197     Harshavardhan Kamarthi
198    </h1>
199     <p class="desc"></p>
200  </header>
201
202  <article>
203    
204    <div class="profile float-right">
205      
206        <img class="img-fluid z-depth-1 rounded" src="/assets/img/prof_pic.jpg">
207      
208      
209    </div>
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212    <div class="clearfix">
213      <p>I’m Harsha. I’m a final-year <a href="https://ml.gatech.edu" target="_blank" rel="noopener noreferrer">Machine Learning PhD student</a> in the Department of Computational Science and Engineering at <a href="https://cc.gatech.edu/" target="_blank" rel="noopener noreferrer">Georgia Institute of Technology</a>. I am affiliated with AdityaLab and am advised by <a href="https://www.cc.gatech.edu/~badityap/" target="_blank" rel="noopener noreferrer">Dr. B Aditya Prakash</a>. I graduated from <a href="https://www.cse.iitm.ac.in" target="_blank" rel="noopener noreferrer">Indian Institute of Technology Madras</a> and am fortunate to have worked with <a href="http://www.cse.iitm.ac.in/~ravi/index.html" target="_blank" rel="noopener noreferrer">Dr. Balaraman Ravindran</a> and <a href="http://www.cse.iitm.ac.in/~sutanuc/" target="_blank" rel="noopener noreferrer">Dr. Sutanu Chakraborti</a>.</p>
214
215<p>My research broadly revolves around robust time-series forecasting and analysis, with a focus on uncertainty, scalability, cross-domain generalization, and operational deployment. My current research interests include:</p>
216
217<ol>
218  <li>Foundational time-series models that are pre-trained on multi-domain datasets and generalize across a wide range of domains and tasks (<a href="https://arxiv.org/abs/2311.11413" target="_blank" rel="noopener noreferrer">LPTM ‘24</a>
218, <a href="https://arxiv.org/abs/2311.07841" target="_blank" rel="noopener noreferrer">PEMS ‘23</a>, <a href="https://arxiv.org/pdf/2402.16132" target="_blank" rel="noopener noreferrer">LSTPrompt ‘24</a>, <a href="https://arxiv.org/abs/2406.08627" target="_blank" rel="noopener noreferrer">Time-MMD ‘24</a>, <a href="https://arxiv.org/abs/2602.20307" target="_blank" rel="noopener noreferrer">ICTP ‘25</a>, <a href="https://arxiv.org/abs/2503.11835" target="_blank" rel="noopener noreferrer">MM4TSA ‘25</a>).</li>
219  <li>Scalable and efficient time-series systems that handle large-scale operational and industrial data while providing robust and calibrated forecasts (<a href="https://github.com/AdityaLab/HAILS" target="_blank" rel="noopener noreferrer">HAILS ‘24</a>, <a href="https://arxiv.org/abs/2206.07940" target="_blank" rel="noopener noreferrer">ProfHiT ‘23</a>, <a href="https://arxiv.org/abs/2601.04432" target="_blank" rel="noopener noreferrer">AHA ‘26</a>).</li>
220  <li>Probabilistic and explainable time-series forecasting models that provide uncertainty estimates and remain robust to outliers, missing data, novel scenarios, and hierarchical constraints (<a href="https://arxiv.org/abs/2407.02641" target="_blank" rel="noopener noreferrer">STOIC ‘24</a>, <a href="https://arxiv.org/abs/2109.07438" target="_blank" rel="noopener noreferrer">CAMUL ‘22</a>, <a href="https://arxiv.org/abs/2106.03904" target="_blank" rel="noopener noreferrer">EPIFNP ‘21</a>, <a href="https://github.com/AdityaLab/Back2Future" target="_blank" rel="noopener noreferrer">B2F ‘22</a>, <a href="https://arxiv.org/abs/2603.06555" target="_blank" rel="noopener noreferrer">HiDeX ‘26</a>).</li>
221</ol>
222
223    </div>
224
225    
226      <div class="news">
227  <h2>news</h2>
228  
229    <div class="table-responsive">
230      <table class="table table-sm table-borderless">
231      
232      
233        <tr>
234          <th scope="row">Mar 6, 2026</th>
235          <td>
236            
237              <a href="https://arxiv.org/abs/2603.06555" target="_blank" rel="noopener noreferrer"><em>Hierarchical Industrial Demand Forecasting with Temporal and Uncertainty Explanations</em></a> is accepted at <strong>ICDE 2026</strong>!
238
239            
240          </td>
241        </tr>
242      
243        <tr>
244          <th scope="row">Jan 7, 2026</th>
245          <td>
246            
247              <a href="https://arxiv.org/abs/2601.04432" target="_blank" rel="noopener noreferrer"><em>AHA: Scalable Alternative History Analysis for Operational Timeseries Applications</em></a> will appear at <strong>KDD 2026</strong>!
248
249            
250          </td>
251        </tr>
252      
253        <tr>
254          <th scope="row">Oct 21, 2025</th>
255          <td>
256            
257              <a href="https://arxiv.org/abs/2602.20307" target="_blank" rel="noopener noreferrer"><em>In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks</em></a> appeared at <strong>CIKM 2025</strong>!
258
259            
260          </td>
261        </tr>
262      
263        <tr>
264          <th scope="row">Mar 15, 2025</th>
265          <td>
266            
267              <a href="https://github.com/AdityaLab/Samay" target="_blank" rel="noopener noreferrer"><em>Samay</em></a> a easy to use library for time-series foundational models is released! Do check it out!
268
269            
270          </td>
271        </tr>
272      
273        <tr>
274          <th scope="row">Mar 14, 2025</th>
275          <td>
276            
277              Our survey <a href="https://arxiv.org/abs/2503.11835" target="_blank" rel="noopener noreferrer"><em>How Can Time Series Analysis Benefit From Multiple Modalities?</em></a> is now available on arXiv.
278
279            
280          </td>
281        </tr>
282      
283      </table>
284    </div>
285  
286</div>
287
288    
289
290    
291      <div class="publications">
292  <h2>publications</h2>
293  <ol class="bibliography">
294<li>
295<div class="row">
296  <div class="col-sm-2 abbr">
297  
298    <img class="img-fluid" src="/assets/pubimg/hidex26.png" alt="hidex26">
299  
300  </div>
301
302  <div id="kamarthi2026hidex" class="col-sm-8">
303    
304      <div class="title">Hierarchical Industrial Demand Forecasting with Temporal and Uncertainty Explanations</div>
305      <div class="author">
306        
307          
308          
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313            
314              
315                <em>Kamarthi, Harshavardhan</em>,
316              
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333                
334                  <a href="https://scholar.google.com/citations?user=LxyfYAUAAAAJ&amp;hl=zh-CN" target="_blank" rel="noopener noreferrer">Xu, Shangqing</a>,
335                
336              
337            
338          
339        
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342          
343          
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345          
346            
347              
348                
349                  Tong, Xinjie,
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359          
360          
361            
362              
363                
364                  Zhou, Xingyu,
365                
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376            
377              
378                
379                  Peters, James,
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391            
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393                
394                  Czyzyk, Joseph,
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410          
411            
412              
413                
414                  and <a href="https://cc.gatech.edu/~badityap" target="_blank" rel="noopener noreferrer">Prakash, B Aditya</a>
415                
416              
417            
418          
419        
420      </div>
421
422      <div class="periodical">
423      
424        <em>ICDE</em>
425      
426      
427        2026
428      
429      </div>
430    
431
432    <div class="links">
433    
434      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
435    
436    
437    
438        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
439    
440    
441    
442      
443      <a href="https://arxiv.org/abs/2603.06555" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
444      
445    
446    
447    
448    
449    
450    
451    
452    </div>
453
454    <!-- Hidden abstract block -->
455    
456    <div class="abstract hidden">
457      <p>Hierarchical time-series forecasting is essential for demand prediction across industries, but the interpretability of such forecasts remains largely unexplored. We introduce an interpretability method for large hierarchical probabilistic time-series forecasting that adapts generic interpretability techniques while addressing hierarchy and uncertainty. The approach explains the significance of time series and external variables at specific time points, the impact of variables on forecast uncertainty, and forecast changes caused by training-data modifications. Experiments on semi-synthetic datasets based on industrial demand scenarios and real-world case studies demonstrate that the method explains state-of-the-art industrial forecasting models with significantly higher explainability accuracy.</p>
458    </div>
459    
460
461    <!-- Hidden bibtex block -->
462    
463    <div class="bibtex hidden">
464      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">kamarthi2026hidex</span><span class="p">,</span>
465  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{hidex26}</span><span class="p">,</span>
466  <span class="na">title</span> <span class="p">=</span> <span class="s">{Hierarchical Industrial Demand Forecasting with Temporal and Uncertainty Explanations}</span><span class="p">,</span>
467  <span class="na">author</span> <span class="p">=</span> <span class="s">{Kamarthi, Harshavardhan and Xu, Shangqing and Tong, Xinjie and Zhou, Xingyu and Peters, James and Czyzyk, Joseph and Prakash, B Aditya}</span><span class="p">,</span>
468  <span class="na">
468journal</span> <span class="p">=</span> <span class="s">{ICDE}</span><span class="p">,</span>
469  <span class="na">year</span> <span class="p">=</span> <span class="s">{2026}</span><span class="p">,</span>
470  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
471  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2603.06555}</span><span class="p">,</span>
472  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span>
473<span class="p">}</span></code></pre></figure>
474    </div>
475    
476  </div>
477</div>
478</li>
479<li>
480<div class="row">
481  <div class="col-sm-2 abbr">
482  
483    <img class="img-fluid" src="/assets/pubimg/aha26.png" alt="aha26">
484  
485  </div>
486
487  <div id="kamarthi2026aha" class="col-sm-8">
488    
489      <div class="title">AHA: Scalable Alternative History Analysis for Operational Timeseries Applications</div>
490      <div class="author">
491        
492          
493          
494          
495          
496          
497          
498            
499              
500                <em>Kamarthi, Harshavardhan</em>,
501              
502            
503          
504        
505          
506          
507          
508          
509          
510          
511            
512              
513                
514                  Shah, Harshil,
515                
516              
517            
518          
519        
520          
521          
522          
523          
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525          
526            
527              
528                
529                  Milner, Henry,
530                
531              
532            
533          
534        
535          
536          
537          
538          
539          
540          
541            
542              
543                
544                  Sinha, Sayan,
545                
546              
547            
548          
549        
550          
551          
552          
553          
554          
555          
556            
557              
558                
559                  Li, Yan,
560                
561              
562            
563          
564        
565          
566          
567          
568          
569            
570              
571                
572                
573          
574          
575          
576            
577              
578                
579                  <a href="https://cc.gatech.edu/~badityap" target="_blank" rel="noopener noreferrer">Prakash, B Aditya</a>,
580                
581              
582            
583          
584        
585          
586          
587          
588          
589          
590          
591            
592              
593                
594                  and Sekar, Vyas
595                
596              
597            
598          
599        
600      </div>
601
602      <div class="periodical">
603      
604        <em>To appear in KDD</em>
605      
606      
607        2026
608      
609      </div>
610    
611
612    <div class="links">
613    
614      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
615    
616    
617    
618        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
619    
620    
621    
622      
623      <a href="https://arxiv.org/abs/2601.04432" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
624      
625    
626    
627    
628    
629    
630    
631    
632    </div>
633
634    <!-- Hidden abstract block -->
635    
636    <div class="abstract hidden">
637      <p>Many operational systems collect high-dimensional timeseries data about users or systems on key performance metrics. Over such historical data, operators and data analysts often need to run retrospective analysis, such as analyzing anomaly detection algorithms, experimenting with different alert configurations, and evaluating new algorithms. We refer to this class of workloads as alternative history analysis for operational datasets. We design and implement AHA, a system that provides cost efficiency and fidelity for high-dimensional data by leveraging decomposable statistics, sparsity in active subpopulations, and efficient aggregation structure in modern analytics databases. Using multiple real-world datasets and production-pipeline case studies at a large video analytics company, AHA provides 100% accuracy for a broad range of downstream tasks and up to 85x lower total cost of ownership compared to conventional methods.</p>
638    </div>
639    
640
641    <!-- Hidden bibtex block -->
642    
643    <div class="bibtex hidden">
644      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">kamarthi2026aha</span><span class="p">,</span>
645  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{aha26}</span><span class="p">,</span>
646  <span class="na">title</span> <span class="p">=</span> <span class="s">{AHA: Scalable Alternative History Analysis for Operational Timeseries Applications}</span><span class="p">,</span>
647  <span class="na">author</span> <span class="p">=</span> <span class="s">{Kamarthi, Harshavardhan and Shah, Harshil and Milner, Henry and Sinha, Sayan and Li, Yan and Prakash, B Aditya and Sekar, 
647Vyas}</span><span class="p">,</span>
648  <span class="na">journal</span> <span class="p">=</span> <span class="s">{To appear in KDD}</span><span class="p">,</span>
649  <span class="na">year</span> <span class="p">=</span> <span class="s">{2026}</span><span class="p">,</span>
650  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
651  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2601.04432}</span><span class="p">,</span>
652  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span>
653<span class="p">}</span></code></pre></figure>
654    </div>
655    
656  </div>
657</div>
658</li>
659<li>
660<div class="row">
661  <div class="col-sm-2 abbr">
662  
663    <img class="img-fluid" src="/assets/pubimg/mm4tsa25.png" alt="mm4tsa25">
664  
665  </div>
666
667  <div id="liu2025mm4tsa" class="col-sm-8">
668    
669      <div class="title">How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlook</div>
670      <div class="author">
671        
672          
673          
674          
675          
676            
677              
678                
679                
680          
681          
682          
683            
684              
685                
686                  <a href="https://twitter.com/HessianLiu" target="_blank" rel="noopener noreferrer">Liu, Haoxin</a>,
687                
688              
689            
690          
691        
692          
693          
694          
695          
696          
697          
698            
699              
700                <em>Kamarthi, Harshavardhan</em>,
701              
702            
703          
704        
705          
706          
707          
708          
709            
710              
711                
712                
713          
714          
715          
716            
717              
718                
719                  <a href="https://twitter.com/leozhao_zhiyuan" target="_blank" rel="noopener noreferrer">Zhao, Zhiyuan</a>,
720                
721              
722            
723          
724        
725          
726          
727          
728          
729            
730              
731                
732                
733          
734          
735          
736            
737              
738                
739                  <a href="https://scholar.google.com/citations?user=LxyfYAUAAAAJ&amp;hl=zh-CN" target="_blank" rel="noopener noreferrer">Xu, Shangqing</a>,
740                
741              
742            
743          
744        
745          
746          
747          
748          
749          
750          
751            
752              
753                
754                  Wang, Shiyu,
755                
756              
757            
758          
759        
760          
761          
762          
763          
764          
765          
766            
767              
768                
769                  Wen, Qingsong,
770                
771              
772            
773          
774        
775          
776          
777          
778          
779          
780          
781            
782              
783                
784                  Hartvigsen, Tom,
785                
786              
787            
788          
789        
790          
791          
792          
793          
794          
795          
796            
797              
798                
799                  Wang, Fei,
800                
801              
802            
803          
804        
805          
806          
807          
808          
809            
810              
811                
812                
813          
814          
815          
816            
817              
818                
819                  and <a href="https://cc.gatech.edu/~badityap" target="_blank" rel="noopener noreferrer">Prakash, B Aditya</a>
820                
821              
822            
823          
824        
825      </div>
826
827      <div class="periodical">
828      
829        <em>arXiv preprint arXiv:2503.11835</em>
830      
831      
832        2025
833      
834      </div>
835    
836
837    <div class="links">
838    
839      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
840    
841    
842    
843        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
844    
845    
846    
847      
848      <a href="https://arxiv.org/abs/2503.11835" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
849      
850    
851    
852    
853    
854      <a href="https://github.com/qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">
854Code</a>
855    
856    
857    
858    
859    </div>
860
861    <!-- Hidden abstract block -->
862    
863    <div class="abstract hidden">
864      <p>Time series analysis is a longstanding research topic with wide real-world significance, but compared with language and vision it remains relatively underexplored and isolated. This survey reviews Multiple Modalities for Time Series Analysis (MM4TSA), an emerging area studying how time series analysis can benefit from richer modalities. We systematically discuss three benefits: reusing foundation models from other modalities for efficient time series analysis, multimodal extension for enhanced time series analysis, and cross-modality interaction for advanced time series analysis. We group works by modality type, including text, images, audio, tables, and others, and identify future opportunities in modality selection, heterogeneous modality combinations, and unseen-task generalization.</p>
865    </div>
866    
867
868    <!-- Hidden bibtex block -->
869    
870    <div class="bibtex hidden">
871      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">liu2025mm4tsa</span><span class="p">,</span>
872  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{mm4tsa25}</span><span class="p">,</span>
873  <span class="na">title</span> <span class="p">=</span> <span class="s">{How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlook}</span><span class="p">,</span>
874  <span class="na">author</span> <span class="p">=</span> <span class="s">{Liu, Haoxin and Kamarthi, Harshavardhan and Zhao, Zhiyuan and Xu, Shangqing and Wang, Shiyu and Wen, Qingsong and Hartvigsen, Tom and Wang, Fei and Prakash, B Aditya}</span><span class="p">,</span>
875  <span class="na">journal</span> <span class="p">=</span> <span class="s">{arXiv preprint arXiv:2503.11835}</span><span class="p">,</span>
876  <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span>
877  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
878  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2503.11835}</span><span class="p">,</span>
879  <span class="na">code</span> <span class="p">=</span> <span class="s">{https://github.com/qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM}</span><span class="p">,</span>
880  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span>
881<span class="p">}</span></code></pre></figure>
882    </div>
883    
884  </div>
885</div>
886</li>
887<li>
888<div class="row">
889  <div class="col-sm-2 abbr">
890  
891    <img class="img-fluid" src="/assets/pubimg/ictp25.png" alt="ictp25">
892  
893  </div>
894
895  <div id="xu2025ictp" class="col-sm-8">
896    
897      <div class="title">In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks</div>
898      <div class="author">
899        
900          
901          
902          
903          
904            
905              
906                
907                
908          
909          
910          
911            
912              
913                
914                  <a href="https://scholar.google.com/citations?user=LxyfYAUAAAAJ&amp;hl=zh-CN" target="_blank" rel="noopener noreferrer">Xu, Shangqing</a>,
915                
916              
917            
918          
919        
920          
921          
922          
923          
924          
925          
926            
927              
928                <em>Kamarthi, Harshavardhan</em>,
929              
930            
931          
932        
933          
934          
935          
936          
937            
938              
939                
940                
941          
942          
943          
944            
945              
946                
947                  <a href="https://twitter.com/HessianLiu" target="_blank" rel="noopener noreferrer">Liu, Haoxin</a>,
948                
949              
950            
951          
952        
953          
954          
955          
956          
957            
958              
959                
960                
961          
962          
963          
964            
965              
966                
967                  and <a href="https://cc.gatech.edu/~badityap" target="_blank" rel="noopener noreferrer">Prakash, B Aditya</a>
968                
969              
970            
971          
972        
973      </div>
974
975      <div class="periodical">
976      
977        <em>In CIKM</em>
978      
979      
980        2025
981      
982      </div>
983    
984
985    <div class="links">
986    
987      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
988    
989    
990    
991        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
992    
993    
994    
995      
996      <a href="https://arxiv.org/abs/2602.20307" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
997      
998    
999    
1000    
1001    
1002    
1003    
1004    
1005    </div>
1006
1007    <!-- Hidden abstract block -->
1008    
1009    <div class="abstract hidden">
1010      <p>Time-series foundation models have demonstrated strong generalization across diverse datasets and tasks, but existing models are usually pre-trained for specific tasks and often struggle to generalize to unseen tasks without fine-tuning. We propose In-Context Time-series Pre-training (ICTP), which restructures pre-training data to equip a backbone time-series foundation model with in-context learning capabilities. ICTP enables test-time adaptation to unseen tasks from input-output relationships provided in context and improves state-of-the-art time-series foundation models by approximately 11.4% on unseen tasks without fine-tuning.</p>
1011    </div>
1012    
1013
1014    <!-- Hidden bibtex block -->
1015    
1016    <div class="bibtex hidden">
1017      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">xu2025ictp</span><span class="p">,</span>
1018  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{ictp25}</span><span class="p">,</span>
1019  <span class="na">title</span> <span class="p">=</span> <span class="s">{In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks}</span><span class="p">,</span>
1020  <span class="na">author</span> <span class="p">=</span> <span class="s">{Xu, Shangqing and Kamarthi, Harshavardhan and Liu, Haoxin and Prakash, B Aditya}</span><span class="p">,</span>
1021  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{CIKM}</span><span class="p">,</span>
1022  <span class="na">pages</span> <span class="p">=</span> <span class="s">{5386--5390}</span><span class="p">,</span>
1023  <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span>
1024  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
1025  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2602.20307}</span><span class="p">,</span>
1026  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span>
1027<span class="p">}</span></code></pre></figure>
1028    </div>
1029    
1030  </div>
1031</div>
1032</li>
1033<li>
1034<div class="row">
1035  <div class="col-sm-2 abbr">
1036  
1037    <img class="img-fluid" src="/assets/pubimg/lptm23.png" alt="lptm23">
1038  
1039  </div>
1040
1041  <div id="kamarthi2023large" class="col-sm-8">
1042    
1043      <div class="title">Large Pre-trained time series models for cross-domain Time series anal
1043ysis tasks</div>
1044      <div class="author">
1045        
1046          
1047          
1048          
1049          
1050          
1051          
1052            
1053              
1054                <em>Kamarthi, Harshavardhan</em>,
1055              
1056            
1057          
1058        
1059          
1060          
1061          
1062          
1063            
1064              
1065                
1066                
1067          
1068          
1069          
1070            
1071              
1072                
1073                  and <a href="https://cc.gatech.edu/~badityap" target="_blank" rel="noopener noreferrer">Prakash, B Aditya</a>
1074                
1075              
1076            
1077          
1078        
1079      </div>
1080
1081      <div class="periodical">
1082      
1083        <em>NeurIPS</em>
1084      
1085      
1086        2024
1087      
1088      </div>
1089    
1090
1091    <div class="links">
1092    
1093      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
1094    
1095    
1096    
1097        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
1098    
1099    
1100    
1101      
1102      <a href="https://arxiv.org/abs/2311.11413" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
1103      
1104    
1105    
1106    
1107    
1108      <a href="https://github.com/kage08/SegmentTS" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">Code</a>
1109    
1110    
1111    
1112    
1113    </div>
1114
1115    <!-- Hidden abstract block -->
1116    
1117    <div class="abstract hidden">
1118      <p>Large pre-trained models have been vital in recent advancements in domains like language and vision, making model training for individual downstream tasks more efficient and provide superior performance. However, tackling time-series analysis tasks usually involves designing and training a separate model from scratch leveraging training data and domain expertise specific to the task. We tackle a significant challenge for pre-training a foundational time-series model from multi-domain time-series datasets: extracting
1119                 semantically useful tokenized inputs to the model
1120                 across heterogenous time-series from different domains. We propose Large Pre-trained Time-series Models (LPTM) that introduces a novel method of adaptive segmentation that automatically identifies optimal dataset-specific
1121                 segmentation strategy during pre-training. This enables
1122                 LPTM to perform similar to or better than domain-specific state-of-art model
1123                 when fine-tuned to different downstream time-series analysis tasks and under zero-shot settings.
1124                 LPTM achieves superior forecasting and time-series classification results
1125                 taking up to 40% less data and 50% less training time
1126                 compared to state-of-art baselines.</p>
1127    </div>
1128    
1129
1130    <!-- Hidden bibtex block -->
1131    
1132    <div class="bibtex hidden">
1133      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">kamarthi2023large</span><span class="p">,</span>
1134  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{lptm23}</span><span class="p">,</span>
1135  <span class="na">title</span> <span class="p">=</span> <span class="s">{Large Pre-trained time series models for cross-domain Time series analysis tasks}</span><span class="p">,</span>
1136  <span class="na">author</span> <span class="p">=</span> <span class="s">{Kamarthi, Harshavardhan and Prakash, B Aditya}</span><span class="p">,</span>
1137  <span class="na">journal</span> <span class="p">=</span> <span class="s">{NeurIPS}</span><span class="p">,</span>
1138  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
1139  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
1140  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2311.11413}</span><span class="p">,</span>
1141  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
1142  <span class="na">code</span> <span class="p">=</span> <span class="s">{https://github.com/kage08/SegmentTS}</span>
1143<span class="p">}</span></code></pre></figure>
1144    </div>
1145    
1146  </div>
1147</div>
1148</li>
1149<li>
1150<div class="row">
1151  <div class="col-sm-2 abbr">
1152  
1153    <img class="img-fluid" src="/assets/pubimg/hails24.png" alt="hails24">
1154  
1155  </div>
1156
1157  <div id="kamarthi2024hails" class="col-sm-8">
1158    
1159      <div class="title">
1159Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity</div>
1160      <div class="author">
1161        
1162          
1163          
1164          
1165          
1166          
1167          
1168            
1169              
1170                <em>Kamarthi, Harshavardhan</em>,
1171              
1172            
1173          
1174        
1175          
1176          
1177          
1178          
1179          
1180          
1181            
1182              
1183                
1184                  Sasanur, Aditya B,
1185                
1186              
1187            
1188          
1189        
1190          
1191          
1192          
1193          
1194          
1195          
1196            
1197              
1198                
1199                  Tong, Xinjie,
1200                
1201              
1202            
1203          
1204        
1205          
1206          
1207          
1208          
1209          
1210          
1211            
1212              
1213                
1214                  Zhou, Xingyu,
1215                
1216              
1217            
1218          
1219        
1220          
1221          
1222          
1223          
1224          
1225          
1226            
1227              
1228                
1229                  Peters, James,
1230                
1231              
1232            
1233          
1234        
1235          
1236          
1237          
1238          
1239          
1240          
1241            
1242              
1243                
1244                  Czyzyk, Joe,
1245                
1246              
1247            
1248          
1249        
1250          
1251          
1252          
1253          
1254            
1255              
1256                
1257                
1258          
1259          
1260          
1261            
1262              
1263                
1264                  and <a href="https://cc.gatech.edu/~badityap" target="_blank" rel="noopener noreferrer">Prakash, B Aditya</a>
1265                
1266              
1267            
1268          
1269        
1270      </div>
1271
1272      <div class="periodical">
1273      
1274        <em>KDD</em>
1275      
1276      
1277        2024
1278      
1279      </div>
1280    
1281
1282    <div class="links">
1283    
1284      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
1285    
1286    
1287    
1288        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
1289    
1290    
1291    
1292      
1293      <a href="https://arxiv.org/abs/2407.02657" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
1294      
1295    
1296    
1297    
1298    
1299      <a href="https://github.com/AdityaLab/HAILS" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">Code</a>
1300    
1301    
1302    
1303    
1304    </div>
1305
1306    <!-- Hidden abstract block -->
1307    
1308    <div class="abstract hidden">
1309      <p>Hierarchical time-series forecasting (HTSF) is an important problem for many real-world business applications where the goal is to simultaneously forecast multiple time-series that are related to each other via a hierarchical relation. Recent works, however, do not address two important challenges that are typically observed in many demand forecasting applications at large companies. First, many time-series at lower levels of the hierarchy have high sparsity i.e., they have a significant number of zeros. Most HTSF methods do not address this varying sparsity across the hierarchy. Further, they do not scale well to the large size of the real-world hierarchy typically unseen in benchmarks used in literature. We resolve both these challenges by proposing HAILS, a novel probabilistic hierarchical model that enables accurate and calibrated probabilistic forecasts across the hierarchy by adaptively modeling sparse and dense time-series with different distributional assumptions and reconciling them to adhere to hierarchical constraints. We show the scalability and effectiveness of our methods by evaluating them against real-world demand forecasting datasets. We deploy HAILS at a large chemical manufacturing company for a product demand forecasting application with over ten thousand products and observe a significant 8.5% improvement in forecast accuracy and 23% better improvement for sparse time-series. The enhanced accuracy and scalability make HAILS a valuable tool for improved business planning and customer experience.</p>
1310    </div>
1311    
1312
1313    <!-- Hidden bibtex block -->
1314    
1315    <div class="bibtex hidden">
1316      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">kamarthi2024hails</span><span class="p">,</span>
1317  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{hails24}</span><span class="p">,</span>
1318  <span class="na">title</span> <span class="p">=</span> <span class="s">
1318{Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity}</span><span class="p">,</span>
1319  <span class="na">author</span> <span class="p">=</span> <span class="s">{Kamarthi, Harshavardhan and Sasanur, Aditya B and Tong, Xinjie and Zhou, Xingyu and Peters, James and Czyzyk, Joe and Prakash, B Aditya}</span><span class="p">,</span>
1320  <span class="na">journal</span> <span class="p">=</span> <span class="s">{KDD}</span><span class="p">,</span>
1321  <span class="na">pages</span> <span class="p">=</span> <span class="s">{5230--5239}</span><span class="p">,</span>
1322  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
1323  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
1324  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2407.02657}</span><span class="p">,</span>
1325  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
1326  <span class="na">code</span> <span class="p">=</span> <span class="s">{https://github.com/AdityaLab/HAILS}</span>
1327<span class="p">}</span></code></pre></figure>
1328    </div>
1329    
1330  </div>
1331</div>
1332</li>
1333<li>
1334<div class="row">
1335  <div class="col-sm-2 abbr">
1336  
1337    <img class="img-fluid" src="/assets/pubimg/foil24.png" alt="foil24">
1338  
1339  </div>
1340
1341  <div id="haoxin2024foil" class="col-sm-8">
1342    
1343      <div class="title">Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning</div>
1344      <div class="author">
1345        
1346          
1347          
1348          
1349          
1350            
1351              
1352                
1353                
1354          
1355          
1356          
1357            
1358              
1359                
1360                  <a href="https://twitter.com/HessianLiu" target="_blank" rel="noopener noreferrer">Liu, Haoxin</a>,
1361                
1362              
1363            
1364          
1365        
1366          
1367          
1368          
1369          
1370          
1371          
1372            
1373              
1374                <em>Kamarthi, Harshavardhan</em>,
1375              
1376            
1377          
1378        
1379          
1380          
1381          
1382          
1383            
1384              
1385                
1386                
1387          
1388          
1389          
1390            
1391              
1392                
1393                  <a href="https://lingkai-kong.com" target="_blank" rel="noopener noreferrer">Kong, Lingkai</a>,
1394                
1395              
1396            
1397          
1398        
1399          
1400          
1401          
1402          
1403            
1404              
1405                
1406                
1407          
1408          
1409          
1410            
1411              
1412                
1413                  <a href="https://twitter.com/leozhao_zhiyuan" target="_blank" rel="noopener noreferrer">Zhao, Zhiyuan</a>,
1414                
1415              
1416            
1417          
1418        
1419          
1420          
1421          
1422          
1423            
1424              
1425                
1426                
1427          
1428          
1429          
1430            
1431              
1432                
1433                  <a href="https://chaozhang.org" target="_blank" rel="noopener noreferrer">Zhang, Chao</a>,
1434                
1435              
1436            
1437          
1438        
1439          
1440          
1441          
1442          
1443            
1444              
1445                
1446                
1447          
1448          
1449          
1450            
1451              
1452                
1453                  and <a href="https://cc.gatech.edu/~badityap" target="_blank" rel="noopener noreferrer">Prakash, B Aditya</a>
1454                
1455              
1456            
1457          
1458        
1459      </div>
1460
1461      <div class="periodical">
1462      
1463        <em>In ICML</em>
1464      
1465      
1466        2024
1467      
1468      </div>
1469    
1470
1471    <div class="links">
1472    
1473      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
1474    
1475    
1476    
1477        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
1478    
1479    
1480    
1481      
1482      <a href="https://openreview.net/forum?id=SMUXPVKUBg" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
1483      
1484    
1485    
1486    
1487    
1488      <a href="https://github.com/AdityaLab/FOIL" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">
1488Code</a>
1489    
1490    
1491    
1492    
1493    </div>
1494
1495    <!-- Hidden abstract block -->
1496    
1497    <div class="abstract hidden">
1498      <p>Time-series forecasting (TSF) finds broad applications in real-world scenarios. Due to the dynamic nature of time-series data, it is crucial for TSF models to preserve out-of-distribution (OOD) generalization abilities, as training and test sets represent historical and future data respectively. In this paper, we aim to alleviate the inherent OOD problem in TSF via invariant learning. We identify fundamental challenges of invariant learning for TSF. First, the target variables in TSF may not be sufficiently determined by the input due to unobserved core variables in TSF, breaking the fundamental assumption of invariant learning. Second, time-series datasets lack adequate environment labels, while existing environmental inference methods are not suitable for TSF. To address these challenges, we propose FOIL, a model-agnostic framework that endows time-series forecasting for out-of-distribution generalization via invariant learning. Specifically, FOIL employs a novel surrogate loss to mitigate the impact of unobserved variables. Further, FOIL implements joint optimization by alternately inferring environments effectively with a multi-head network while preserving the temporal adjacency structure and learning invariant representations across inferred environments for OOD generalized TSF. Extensive experiments demonstrate that the proposed FOIL significantly and consistently improves the performance of various TSF models, achieving gains of up to 85%.</p>
1499    </div>
1500    
1501
1502    <!-- Hidden bibtex block -->
1503    
1504    <div class="bibtex hidden">
1505      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">haoxin2024foil</span><span class="p">,</span>
1506  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{foil24}</span><span class="p">,</span>
1507  <span class="na">title</span> <span class="p">=</span> <span class="s">{Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning}</span><span class="p">,</span>
1508  <span class="na">author</span> <span class="p">=</span> <span class="s">{Liu, Haoxin and Kamarthi, Harshavardhan and Kong, Lingkai and Zhao, Zhiyuan and Zhang, Chao and Prakash, B Aditya}</span><span class="p">,</span>
1509  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{ICML}</span><span class="p">,</span>
1510  <span class="na">pages</span> <span class="p">=</span> <span class="s">{31312--31325}</span><span class="p">,</span>
1511  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
1512  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
1513  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://openreview.net/forum?id=SMUXPVKUBg}</span><span class="p">,</span>
1514  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
1515  <span class="na">code</span> <span class="p">=</span> <span class="s">{https://github.com/AdityaLab/FOIL}</span>
1516<span class="p">}</span></code></pre></figure>
1517    </div>
1518    
1519  </div>
1520</div>
1521</li>
1522<li>
1523<div class="row">
1524  <div class="col-sm-2 abbr">
1525  
1526    <img class="img-fluid" src="/assets/pubimg/lstprompt24.png" alt="lstprompt24">
1527  
1528  </div>
1529
1530  <div id="liu2024lstprompt" class="col-sm-8">
1531    
1532      <div class="title">Lstprompt: Large language models as zero-shot time series forecasters by long-short-term prompting</div>
1533      <div class="author">
1534        
1535          
1536          
1537          
1538          
1539            
1540              
1541                
1542                
1543          
1544          
1545          
1546            
1547              
1548                
1549                  <a href="https://twitter.com/HessianLiu" target="_blank" rel="noopener noreferrer">Liu, Haoxin</a>,
1550                
1551              
1552            
1553          
1554        
1555          
1556          
1557          
1558          
1559            
1560              
1561                
1562                
1563          
1564          
1565          
1566            
1567              
1568                
1569                  <a href="https://twitter.com/leozhao_zhiyuan" target="_blank" rel="noopener noreferrer">Zhao, Zhiyuan</a>,
1570                
1571              
1572            
1573          
1574        
1575          
1576          
1577          
1578          
1579          
1580          
1581            
1582              
1583                
1584                  Wang, Jindong,
1585                
1586              
1587            
1588          
1589        
1590          
1591          
1592          
1593          
1594          
1595          
1596            
1597              
1598                <em>Kamarthi, Harshavardhan</em>,
1599              
1600            
1601          
1602        
1603          
1604          
1605          
1606          
1607            
1608              
1609                
1610                
1611          
1612          
1613          
1614            
1615              
1616                
1617                  and <a href="https://cc.gatech.edu/~badityap" target="_blank" rel="noopener noreferrer">Prakash, B Aditya</a>
1618                
1619              
1620            
1621          
1622        
1623      </div>
1624
1625      <div class="periodical">
1626      
1627        <em>ACL Findings</em>
1628      
1629      
1630        2024
1631      
1632      </div>
1633    
1634
1635    <div class="links">
1636    
1637      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
1638    
1639    
1640    
1641        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
1642    
1643    
1644    
1645      
1646      <a href="https://arxiv.org/pdf/2402.16132" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
1647      
1648    
1649    
1650    
1651    
1652      <a href="https://github.com/AdityaLab/lstprompt" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">
1652Code</a>
1653    
1654    
1655    
1656    
1657    </div>
1658
1659    <!-- Hidden abstract block -->
1660    
1661    <div class="abstract hidden">
1662      <p>Time-series forecasting (TSF) finds broad applications in real-world scenarios. Prompting off-the-shelf Large Language Models (LLMs) demonstrates strong zero-shot TSF capabilities while preserving computational efficiency. However, existing prompting methods oversimplify TSF as language next-token predictions, overlooking its dynamic nature and lack of integration with state-of-the-art prompt strategies such as Chain-of-Thought. Thus, we propose LSTPrompt, a novel approach for prompting LLMs in zero-shot TSF tasks. LSTPrompt decomposes TSF into short-term and long-term forecasting sub-tasks, tailoring prompts to each. LSTPrompt guides LLMs to regularly reassess forecasting mechanisms to enhance adaptability. Extensive evaluations demonstrate consistently better performance of LSTPrompt than existing prompting methods, and competitive results compared to foundation TSF models.</p>
1663    </div>
1664    
1665
1666    <!-- Hidden bibtex block -->
1667    
1668    <div class="bibtex hidden">
1669      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">liu2024lstprompt</span><span class="p">,</span>
1670  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{lstprompt24}</span><span class="p">,</span>
1671  <span class="na">title</span> <span class="p">=</span> <span class="s">{Lstprompt: Large language models as zero-shot time series forecasters by long-short-term prompting}</span><span class="p">,</span>
1672  <span class="na">author</span> <span class="p">=</span> <span class="s">{Liu, Haoxin and Zhao, Zhiyuan and Wang, Jindong and Kamarthi, Harshavardhan and Prakash, B Aditya}</span><span class="p">,</span>
1673  <span class="na">journal</span> <span class="p">=</span> <span class="s">{ACL Findings}</span><span class="p">,</span>
1674  <span class="na">pages</span> <span class="p">=</span> <span class="s">{7832--7840}</span><span class="p">,</span>
1675  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
1676  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
1677  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/pdf/2402.16132}</span><span class="p">,</span>
1678  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
1679  <span class="na">code</span> <span class="p">=</span> <span class="s">{https://github.com/AdityaLab/lstprompt}</span>
1680<span class="p">}</span></code></pre></figure>
1681    </div>
1682    
1683  </div>
1684</div>
1685</li>
1686<li>
1687<div class="row">
1688  <div class="col-sm-2 abbr">
1689  
1690    <img class="img-fluid" src="/assets/pubimg/timemmd24.png" alt="timemmd24">
1691  
1692  </div>
1693
1694  <div id="liu2024time" class="col-sm-8">
1695    
1696      <div class="title">Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis</div>
1697      <div class="author">
1698        
1699          
1700          
1701          
1702          
1703            
1704              
1705                
1706                
1707          
1708          
1709          
1710            
1711              
1712                
1713                  <a href="https://twitter.com/HessianLiu" target="_blank" rel="noopener noreferrer">Liu, Haoxin</a>,
1714                
1715              
1716            
1717          
1718        
1719          
1720          
1721          
1722          
1723            
1724              
1725                
1726                
1727          
1728          
1729          
1730            
1731              
1732                
1733                  <a href="https://scholar.google.com/citations?user=LxyfYAUAAAAJ&amp;hl=zh-CN" target="_blank" rel="noopener noreferrer">Xu, Shangqing</a>,
1734                
1735              
1736            
1737          
1738        
1739          
1740          
1741          
1742          
1743            
1744              
1745                
1746                
1747          
1748          
1749          
1750            
1751              
1752                
1753                  <a href="https://twitter.com/leozhao_zhiyuan" target="_blank" rel="noopener noreferrer">Zhao, Zhiyuan</a>,
1754                
1755              
1756            
1757          
1758        
1759          
1760          
1761          
1762          
1763            
1764              
1765                
1766                
1767          
1768          
1769          
1770            
1771              
1772                
1773                  <a href="https://lingkai-kong.com" target="_blank" rel="noopener noreferrer">Kong, Lingkai</a>,
1774                
1775              
1776            
1777          
1778        
1779          
1780          
1781          
1782          
1783          
1784          
1785            
1786              
1787                <em>Kamarthi, Harshavardhan</em>,
1788              
1789            
1790          
1791        
1792          
1793          
1794          
1795          
1796          
1797          
1798            
1799              
1800                
1801                  Sasanur, Aditya B,
1802                
1803              
1804            
1805          
1806        
1807          
1808          
1809          
1810          
1811          
1812          
1813            
1814              
1815                
1816                  Sharma, Megha,
1817                
1818              
1819            
1820          
1821        
1822          
1823          
1824          
1825          
1826          
1827          
1828            
1829              
1830                
1831                  Cui, Jiaming,
1832                
1833              
1834            
1835          
1836        
1837          
1838          
1839          
1840          
1841          
1842          
1843            
1844              
1845                
1846                  Wen, Qingsong,
1847                
1848              
1849            
1850          
1851        
1852          
1853          
1854          
1855          
1856            
1857              
1858                
1859                
1860          
1861          
1862          
1863            
1864              
1865                
1866                  <a href="https://chaozhang.org" target="_blank" rel="noopener noreferrer">Zhang, Chao</a>,
1867                
1868              
1869            
1870          
1871        
1872          
1873          
1874          
1875          
1876          
1877          
1878            
1879              
1880                
1881                  and others, 
1882                
1883              
1884            
1885          
1886        
1887      </div>
1888
1889      <div class="periodical">
1890      
1891        <em>NeurIPS</em>
1892      
1893      
1894        2024
1895      
1896      </div>
1897    
1898
1899    <div class="links">
1900    
1901      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
1902    
1903    
1904    
1905        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
1906    
1907    
1908    
1909      
1910      <a href="https://arxiv.org/abs/2406.08627" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
1911      
1912    
1913    
1914    
1915    
1916      <a href="https://github.com/AdityaLab/Time-MMD" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">
1916Code</a>
1917    
1918    
1919    
1920    
1921    </div>
1922
1923    <!-- Hidden abstract block -->
1924    
1925    <div class="abstract hidden">
1926      <p>Time series data are ubiquitous across a wide range of real-world domains. While real-world time series analysis (TSA) requires human experts to integrate numerical series data with multimodal domain-specific knowledge, most existing TSA models rely solely on numerical data, overlooking the significance of information beyond numerical series. This oversight is due to the untapped potential of textual series data and the absence of a comprehensive, high-quality multimodal dataset. To overcome this obstacle, we introduce Time-MMD, the first multi-domain, multimodal time series dataset covering 9 primary data domains. Time-MMD ensures fine-grained modality alignment, eliminates data contamination, and provides high usability. Additionally, we develop MM-TSFlib, the first multimodal time-series forecasting (TSF) library, seamlessly pipelining multimodal TSF evaluations based on Time-MMD for in-depth analyses. Extensive experiments conducted on Time-MMD through MM-TSFlib demonstrate significant performance enhancements by extending unimodal TSF to multimodality, evidenced by over 15% mean squared error reduction in general, and up to 40% in domains with rich textual data. More importantly, our datasets and library revolutionize broader applications, impacts, research topics to advance TSA.</p>
1927    </div>
1928    
1929
1930    <!-- Hidden bibtex block -->
1931    
1932    <div class="bibtex hidden">
1933      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">liu2024time</span><span class="p">,</span>
1934  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{timemmd24}</span><span class="p">,</span>
1935  <span class="na">title</span> <span class="p">=</span> <span class="s">{Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis}</span><span class="p">,</span>
1936  <span class="na">author</span> <span class="p">=</span> <span class="s">{Liu, Haoxin and Xu, Shangqing and Zhao, Zhiyuan and Kong, Lingkai and Kamarthi, Harshavardhan and Sasanur, Aditya B and Sharma, Megha and Cui, Jiaming and Wen, Qingsong and Zhang, Chao and others}</span><span class="p">,</span>
1937  <span class="na">journal</span> <span class="p">=</span> <span class="s">{NeurIPS}</span><span class="p">,</span>
1938  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
1939  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
1940  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2406.08627}</span><span class="p">,</span>
1941  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
1942  <span class="na">code</span> <span class="p">=</span> <span class="s">{https://github.com/AdityaLab/Time-MMD}</span>
1943<span class="p">}</span></code></pre></figure>
1944    </div>
1945    
1946  </div>
1947</div>
1948</li>
1949<li>
1950<div class="row">
1951  <div class="col-sm-2 abbr">
1952  
1953    <img class="img-fluid" src="/assets/pubimg/nature24.png" alt="nature24">
1954  
1955  </div>
1956
1957  <div id="mathis2024title" class="col-sm-8">
1958    
1959      <div class="title">Title evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations</div>
1960      <div class="author">
1961        
1962          
1963          
1964          
1965          
1966          
1967          
1968            
1969              
1970                
1971                  Mathis, Sarabeth M,
1972                
1973              
1974            
1975          
1976        
1977          
1978          
1979          
1980          
1981          
1982          
1983            
1984              
1985                
1986                  Webber, Alexander E,
1987                
1988              
1989            
1990          
1991        
1992          
1993          
1994          
1995          
1996          
1997          
1998            
1999              
2000                
2001                  León, Tomás M,
2002                
2003              
2004            
2005          
2006        
2007          
2008          
2009          
2010          
2011          
2012          
2013            
2014              
2015                
2016                  Murray, Erin L,
2017                
2018              
2019            
2020          
2021        
2022          
2023          
2024          
2025          
2026          
2027          
2028            
2029              
2030                
2031                  Sun, Monica,
2032                
2033              
2034            
2035          
2036        
2037          
2038          
2039          
2040          
2041          
2042          
2043            
2044              
2045                
2046                  White, Lauren A,
2047                
2048              
2049            
2050          
2051        
2052          
2053          
2054          
2055          
2056          
2057          
2058            
2059              
2060                
2061                  Brooks, Logan C,
2062                
2063              
2064            
2065          
2066        
2067          
2068          
2069          
2070          
2071          
2072          
2073            
2074              
2075                
2076                  Green, Alden,
2077                
2078              
2079            
2080          
2081        
2082          
2083          
2084          
2085          
2086          
2087          
2088            
2089              
2090                
2091                  Hu, Addison J,
2092                
2093              
2094            
2095          
2096        
2097          
2098          
2099          
2100          
2101          
2102          
2103            
2104              
2105                
2106                  Rosenfeld, Roni,
2107                
2108              
2109            
2110          
2111        
2112          
2113          
2114          
2115          
2116          
2117          
2118            
2119              
2120                
2121                  and others, 
2122                
2123              
2124            
2125          
2126        
2127      </div>
2128
2129      <div class="periodical">
2130      
2131        <em>Nature Communications</em>
2132      
2133      
2134        2024
2135      
2136      </div>
2137    
2138
2139    <div class="links">
2140    
2141      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
2142    
2143    
2144    
2145        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
2146    
2147    
2148    
2149      
2150      <a href="https://www.nature.com/articles/s41467-024-50601-9" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
2151      
2152    
2153    
2154    
2155    
2156    
2157    
2158    
2159    </div>
2160
2161    <!-- Hidden abstract block -->
2162    
2163    <div class="abstract hidden">
2164      <p>Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021–22 and 2022–23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021–22 and 12 out of 18 models in 2022–23. Averaging across all forecast targets, the FluSight ensemble is the 2nd most accurate model measured by WIS in 2021–22 and the 5th most accurate in the 2022–23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.</p>
2165    </div>
2166    
2167
2168    <!-- Hidden bibtex block -->
2169    
2170    <div class="bibtex hidden">
2171      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">mathis2024title</span><span class="p">,</span>
2172  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{nature24}</span><span class="p">,</span>
2173  <span class="na">title</span> <span class="p">=</span> <span class="s">{Title evaluation of FluSight influenza forecasting in the 2021--22 and 2022--23 seasons with a new target laboratory-confirmed influenza hospital
2173izations}</span><span class="p">,</span>
2174  <span class="na">author</span> <span class="p">=</span> <span class="s">{Mathis, Sarabeth M and Webber, Alexander E and Le{\'o}n, Tom{\'a}s M and Murray, Erin L and Sun, Monica and White, Lauren A and Brooks, Logan C and Green, Alden and Hu, Addison J and Rosenfeld, Roni and others}</span><span class="p">,</span>
2175  <span class="na">journal</span> <span class="p">=</span> <span class="s">{Nature Communications}</span><span class="p">,</span>
2176  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
2177  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
2178  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://www.nature.com/articles/s41467-024-50601-9}</span><span class="p">,</span>
2179  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span>
2180<span class="p">}</span></code></pre></figure>
2181    </div>
2182    
2183  </div>
2184</div>
2185</li>
2186<li>
2187<div class="row">
2188  <div class="col-sm-2 abbr">
2189  
2190    <img class="img-fluid" src="/assets/pubimg/stoic24.png" alt="stoic24">
2191  
2192  </div>
2193
2194  <div id="kamarthi2024stoic" class="col-sm-8">
2195    
2196      <div class="title">Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting</div>
2197      <div class="author">
2198        
2199          
2200          
2201          
2202          
2203          
2204          
2205            
2206              
2207                <em>Kamarthi, Harshavardhan</em>,
2208              
2209            
2210          
2211        
2212          
2213          
2214          
2215          
2216            
2217              
2218                
2219                
2220          
2221          
2222          
2223            
2224              
2225                
2226                  <a href="https://lingkai-kong.com" target="_blank" rel="noopener noreferrer">Kong, Lingkai</a>,
2227                
2228              
2229            
2230          
2231        
2232          
2233          
2234          
2235          
2236            
2237              
2238                
2239                
2240          
2241          
2242          
2243            
2244              
2245                
2246                  <a href="https://alrodri.engin.umich.edu/" target="_blank" rel="noopener noreferrer">Rodriguez, Alexander</a>,
2247                
2248              
2249            
2250          
2251        
2252          
2253          
2254          
2255          
2256            
2257              
2258                
2259                
2260          
2261          
2262          
2263            
2264              
2265                
2266                  <a href="https://chaozhang.org" target="_blank" rel="noopener noreferrer">Zhang, Chao</a>,
2267                
2268              
2269            
2270          
2271        
2272          
2273          
2274          
2275          
2276            
2277              
2278                
2279                
2280          
2281          
2282          
2283            
2284              
2285                
2286                  and <a href="https://cc.gatech.edu/~badityap" target="_blank" rel="noopener noreferrer">Prakash, B Aditya</a>
2287                
2288              
2289            
2290          
2291        
2292      </div>
2293
2294      <div class="periodical">
2295      
2296        <em>KDD 2024 Workshop on Uncertainty Reasoning and Quantification in Decision Making</em>
2297      
2298      
2299        2024
2300      
2301      </div>
2302    
2303
2304    <div class="links">
2305    
2306      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
2307    
2308    
2309    
2310        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
2311    
2312    
2313    
2314      
2315      <a href="https://arxiv.org/abs/2407.02641" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
2316      
2317    
2318    
2319    
2320    
2321      <a href="https://github.com/AdityaLab/STOIC" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">Code</a>
2322    
2323    
2324    
2325    
2326    </div>
2327
2328    <!-- Hidden abstract block -->
2329    
2330    <div class="abstract hidden">
2331      <p>Multi-variate time series forecasting is an important problem with a wide range of applications. Recent works model the relations between time-series as graphs and have shown that propagating information over the relation graph can improve time series forecasting. However, in many cases, relational information is not available or is noisy and reliable. Moreover, most works ignore the underlying uncertainty of time-series both for structure learning and deriving the forecasts resulting in the structure not capturing the uncertainty resulting in forecast distributions with poor uncertainty estimates. We tackle this challenge and introduce STOIC, that leverages stochastic correlations between time-series to learn underlying structure between time-series and to provide well-calibrated and accurate forecasts. Over a wide-range of benchmark datasets STOIC provides around 16% more accurate and 14% better-calibrated forecasts.
2332                 STOIC also shows better adaptation to noise in data during inference and captures important and useful relational information in various benchmarks.</p>
2333    </div>
2334    
2335
2336    <!-- Hidden bibtex block -->
2337    
2338    <div class="bibtex hidden">
2339      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">kamarthi2024stoic</span><span class="p">,</span>
2340  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{stoic24}</span><span class="p">,</span>
2341  <span class="na">title</span> <span class="p">=</span> <span class="s">{Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forec
2341asting}</span><span class="p">,</span>
2342  <span class="na">author</span> <span class="p">=</span> <span class="s">{Kamarthi, Harshavardhan and Kong, Lingkai and Rodriguez, Alexander and Zhang, Chao and Prakash, B Aditya}</span><span class="p">,</span>
2343  <span class="na">journal</span> <span class="p">=</span> <span class="s">{KDD 2024 Workshop on Uncertainty Reasoning and Quantification in Decision Making}</span><span class="p">,</span>
2344  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
2345  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
2346  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2407.02641}</span><span class="p">,</span>
2347  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
2348  <span class="na">code</span> <span class="p">=</span> <span class="s">{https://github.com/AdityaLab/STOIC}</span>
2349<span class="p">}</span></code></pre></figure>
2350    </div>
2351    
2352  </div>
2353</div>
2354</li>
2355<li>
2356<div class="row">
2357  <div class="col-sm-2 abbr">
2358  
2359    <img class="img-fluid" src="/assets/pubimg/survey24.png" alt="survey24">
2360  
2361  </div>
2362
2363  <div id="rodriguez2024machine" class="col-sm-8">
2364    
2365      <div class="title">Machine learning for data-centric epidemic forecasting</div>
2366      <div class="author">
2367        
2368          
2369          
2370          
2371          
2372          
2373          
2374            
2375              
2376                
2377                  Rodrı́guez, Alexander,
2378                
2379              
2380            
2381          
2382        
2383          
2384          
2385          
2386          
2387          
2388          
2389            
2390              
2391                <em>Kamarthi, Harshavardhan</em>,
2392              
2393            
2394          
2395        
2396          
2397          
2398          
2399          
2400          
2401          
2402            
2403              
2404                
2405                  Agarwal, Pulak,
2406                
2407              
2408            
2409          
2410        
2411          
2412          
2413          
2414          
2415          
2416          
2417            
2418              
2419                
2420                  Ho, Javen,
2421                
2422              
2423            
2424          
2425        
2426          
2427          
2428          
2429          
2430          
2431          
2432            
2433              
2434                
2435                  Patel, Mira,
2436                
2437              
2438            
2439          
2440        
2441          
2442          
2443          
2444          
2445          
2446          
2447            
2448              
2449                
2450                  Sapre, Suchet,
2451                
2452              
2453            
2454          
2455        
2456          
2457          
2458          
2459          
2460            
2461              
2462                
2463                
2464          
2465          
2466          
2467            
2468              
2469                
2470                  and <a href="https://cc.gatech.edu/~badityap" target="_blank" rel="noopener noreferrer">Prakash, B Aditya</a>
2471                
2472              
2473            
2474          
2475        
2476      </div>
2477
2478      <div class="periodical">
2479      
2480        <em>Nature Machine Intelligence</em>
2481      
2482      
2483        2024
2484      
2485      </div>
2486    
2487
2488    <div class="links">
2489    
2490      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
2491    
2492    
2493    
2494        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
2495    
2496    
2497    
2498      
2499      <a href="https://www.nature.com/articles/s42256-024-00895-7" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
2500      
2501    
2502    
2503    
2504    
2505    
2506    
2507    
2508    </div>
2509
2510    <!-- Hidden abstract block -->
2511    
2512    <div class="abstract hidden">
2513      <p>The COVID-19 pandemic emphasized the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy. Forecasting epidemic progression is a non-trivial task due to multiple confounding factors, such as human behaviour, pathogen dynamics and environmental conditions. However, the surge in research interest and initiatives from public health and funding agencies has fuelled the availability of new data sources that capture previously unobservable aspects of disease spread, paving the way for a spate of ‘data-centred’ computational solutions that show promise for enhancing our forecasting capabilities. Here we discuss various methodological and practical advances and introduce a conceptual framework to navigate through them. First we list relevant datasets, such as symptomatic online surveys, retail and commerce, mobility and genomics data. Next we consider methods, focusing on re
2513cent data-driven statistical and deep learning-based methods, as well as hybrid models that combine domain knowledge of mechanistic models with the flexibility of statistical approaches. We also discuss experiences and challenges that arise in the real-world deployment of these forecasting systems, including decision-making informed by forecasts. Finally, we highlight some challenges and open problems found across the forecasting pipeline to enable robust future pandemic preparedness.</p>
2514    </div>
2515    
2516
2517    <!-- Hidden bibtex block -->
2518    
2519    <div class="bibtex hidden">
2520      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">rodriguez2024machine</span><span class="p">,</span>
2521  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{survey24}</span><span class="p">,</span>
2522  <span class="na">title</span> <span class="p">=</span> <span class="s">{Machine learning for data-centric epidemic forecasting}</span><span class="p">,</span>
2523  <span class="na">author</span> <span class="p">=</span> <span class="s">{Rodr{\'\i}guez, Alexander and Kamarthi, Harshavardhan and Agarwal, Pulak and Ho, Javen and Patel, Mira and Sapre, Suchet and Prakash, B Aditya}</span><span class="p">,</span>
2524  <span class="na">journal</span> <span class="p">=</span> <span class="s">{Nature Machine Intelligence}</span><span class="p">,</span>
2525  <span class="na">volume</span> <span class="p">=</span> <span class="s">{6}</span><span class="p">,</span>
2526  <span class="na">number</span> <span class="p">=</span> <span class="s">{10}</span><span class="p">,</span>
2527  <span class="na">pages</span> <span class="p">=</span> <span class="s">{1122--1131}</span><span class="p">,</span>
2528  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
2529  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
2530  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://www.nature.com/articles/s42256-024-00895-7}</span><span class="p">,</span>
2531  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span>
2532<span class="p">}</span></code></pre></figure>
2533    </div>
2534    
2535  </div>
2536</div>
2537</li>
2538<li>
2539<div class="row">
2540  <div class="col-sm-2 abbr">
2541  
2542    <img class="img-fluid" src="/assets/pubimg/pems23.png" alt="pems23">
2543  
2544  </div>
2545
2546  <div id="kamarthi2023pems" class="col-sm-8">
2547    
2548      <div class="title">PEMS: Pre-trained Epidmic Time-series Models</div>
2549      <div class="author">
2550        
2551          
2552          
2553          
2554          
2555          
2556          
2557            
2558              
2559                <em>Kamarthi, Harshavardhan</em>,
2560              
2561            
2562          
2563        
2564          
2565          
2566          
2567          
2568            
2569              
2570                
2571                
2572          
2573          
2574          
2575            
2576              
2577                
2578                  and <a href="https://cc.gatech.edu/~badityap" target="_blank" rel="noopener noreferrer">Prakash, B Aditya</a>
2579                
2580              
2581            
2582          
2583        
2584      </div>
2585
2586      <div class="periodical">
2587      
2588        <em>arXiv preprint arXiv:2311.07841</em>
2589      
2590      
2591        2023
2592      
2593      </div>
2594    
2595
2596    <div class="links">
2597    
2598      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
2599    
2600    
2601    
2602        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
2603    
2604    
2605    
2606      
2607      <a href="https://arxiv.org/abs/2311.07841" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
2608      
2609    
2610    
2611    
2612    
2613    
2614    
2615    
2616    </div>
2617
2618    <!-- Hidden abstract block -->
2619    
2620    <div class="abstract hidden">
2621      <p>Providing accurate and reliable predictions about the future of an epidemic is an important problem for enabling informed public health decisions. Recent works have shown that leveraging data-driven solutions that utilize advances in deep learning methods to learn from past data of an epidemic often outperform traditional mechanistic models. However, in many cases, the past data is sparse and may not sufficiently capture the underlying dynamics. While there exists a large amount of data from past epidemics, leveraging prior knowledge from time-series data of other diseases is a non-trivial challenge. Motivated by the success of pre-trained models in language and vision tasks, we tackle the problem of pre-training epidemic time-series models to learn from multiple datasets from different diseases and epidemics. We introduce Pre-trained Epidemic Time-Series Models (PEMS) that learn from diverse time-series datasets of a variety of diseases by formulating pre-training as a set of self-supervised learning (SSL) tasks. We tackle various important challenges specific to pre-training for epidemic time-series such as dealing with heterogeneous dynamics and efficie
2621ntly capturing useful patterns from multiple epidemic datasets by carefully designing the SSL tasks to learn important priors about the epidemic dynamics that can be leveraged for fine-tuning to multiple downstream tasks. The resultant PEM outperforms previous state-of-the-art methods in various downstream time-series tasks across datasets of varying seasonal patterns, geography, and mechanism of contagion including the novel Covid-19 pandemic unseen in pre-trained data with better efficiency using smaller fraction of datasets.</p>
2622    </div>
2623    
2624
2625    <!-- Hidden bibtex block -->
2626    
2627    <div class="bibtex hidden">
2628      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">kamarthi2023pems</span><span class="p">,</span>
2629  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{pems23}</span><span class="p">,</span>
2630  <span class="na">title</span> <span class="p">=</span> <span class="s">{PEMS: Pre-trained Epidmic Time-series Models}</span><span class="p">,</span>
2631  <span class="na">author</span> <span class="p">=</span> <span class="s">{Kamarthi, Harshavardhan and Prakash, B Aditya}</span><span class="p">,</span>
2632  <span class="na">journal</span> <span class="p">=</span> <span class="s">{arXiv preprint arXiv:2311.07841}</span><span class="p">,</span>
2633  <span class="na">year</span> <span class="p">=</span> <span class="s">{2023}</span><span class="p">,</span>
2634  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
2635  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2311.07841}</span><span class="p">,</span>
2636  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span>
2637<span class="p">}</span></code></pre></figure>
2638    </div>
2639    
2640  </div>
2641</div>
2642</li>
2643<li>
2644<div class="row">
2645  <div class="col-sm-2 abbr">
2646  
2647    <img class="img-fluid" src="/assets/pubimg/profhit23.png" alt="profhit23">
2648  
2649  </div>
2650
2651  <div id="kamarthi2023profhit" class="col-sm-8">
2652    
2653      <div class="title">PROFHIT: Probabilistic Robust Forecasting for Hierarchical Time-series</div>
2654      <div class="author">
2655        
2656          
2657          
2658          
2659          
2660          
2661          
2662            
2663              
2664                <em>Kamarthi, Harshavardhan</em>,
2665              
2666            
2667          
2668        
2669          
2670          
2671          
2672          
2673            
2674              
2675                
2676                
2677          
2678          
2679          
2680            
2681              
2682                
2683                  <a href="https://lingkai-kong.com" target="_blank" rel="noopener noreferrer">Kong, Lingkai</a>,
2684                
2685              
2686            
2687          
2688        
2689          
2690          
2691          
2692          
2693          
2694          
2695            
2696              
2697                
2698                  Rodrı́guez, Alexander,
2699                
2700              
2701            
2702          
2703        
2704          
2705          
2706          
2707          
2708            
2709              
2710                
2711                
2712          
2713          
2714          
2715            
2716              
2717                
2718                  <a href="https://chaozhang.org" target="_blank" rel="noopener noreferrer">Zhang, Chao</a>,
2719                
2720              
2721            
2722          
2723        
2724          
2725          
2726          
2727          
2728            
2729              
2730                
2731                
2732          
2733          
2734          
2735            
2736              
2737                
2738                  and <a href="https://cc.gatech.edu/~badityap" target="_blank" rel="noopener noreferrer">Prakash, B Aditya</a>
2739                
2740              
2741            
2742          
2743        
2744      </div>
2745
2746      <div class="periodical">
2747      
2748        <em>KDD</em>
2749      
2750      
2751        2023
2752      
2753      </div>
2754    
2755
2756    <div class="links">
2757    
2758      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
2759    
2760    
2761    
2762        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
2763    
2764    
2765    
2766      
2767      <a href="https://arxiv.org/abs/2206.07940" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
2768      
2769    
2770    
2771    
2772    
2773      <a href="https://github.com/AdityaLab/CAMul" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">
2773Code</a>
2774    
2775    
2776    
2777    
2778    </div>
2779
2780    <!-- Hidden abstract block -->
2781    
2782    <div class="abstract hidden">
2783      <p>Many multivariate time-series that have underlying hierarchical relations. Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series. We propose PROFHIT, a state-of-art probabilistic model that provides accurate probabilitic forecasts that adhere to hierarchical relations at distributional level and adapts to datasets with different levels of adherance to the hierarchy.</p>
2784    </div>
2785    
2786
2787    <!-- Hidden bibtex block -->
2788    
2789    <div class="bibtex hidden">
2790      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">kamarthi2023profhit</span><span class="p">,</span>
2791  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{profhit23}</span><span class="p">,</span>
2792  <span class="na">title</span> <span class="p">=</span> <span class="s">{PROFHIT: Probabilistic Robust Forecasting for Hierarchical Time-series}</span><span class="p">,</span>
2793  <span class="na">author</span> <span class="p">=</span> <span class="s">{Kamarthi, Harshavardhan and Kong, Lingkai and Rodr{\'\i}guez, Alexander and Zhang, Chao and Prakash, B Aditya}</span><span class="p">,</span>
2794  <span class="na">journal</span> <span class="p">=</span> <span class="s">{KDD}</span><span class="p">,</span>
2795  <span class="na">year</span> <span class="p">=</span> <span class="s">{2023}</span><span class="p">,</span>
2796  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
2797  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2206.07940}</span><span class="p">,</span>
2798  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
2799  <span class="na">code</span> <span class="p">=</span> <span class="s">{https://github.com/AdityaLab/CAMul}</span>
2800<span class="p">}</span></code></pre></figure>
2801    </div>
2802    
2803  </div>
2804</div>
2805</li>
2806<li>
2807<div class="row">
2808  <div class="col-sm-2 abbr">
2809  
2810    <img class="img-fluid" src="/assets/pubimg/camul21.png" alt="camul21">
2811  
2812  </div>
2813
2814  <div id="kamarthi2021camul" class="col-sm-8">
2815    
2816      <div class="title">CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting</div>
2817      <div class="author">
2818        
2819          
2820          
2821          
2822          
2823          
2824          
2825            
2826              
2827                <em>Kamarthi, Harshavardhan</em>,
2828              
2829            
2830          
2831        
2832          
2833          
2834          
2835          
2836            
2837              
2838                
2839                
2840          
2841          
2842          
2843            
2844              
2845                
2846                  <a href="https://lingkai-kong.com" target="_blank" rel="noopener noreferrer">Kong, Lingkai</a>,
2847                
2848              
2849            
2850          
2851        
2852          
2853          
2854          
2855          
2856          
2857          
2858            
2859              
2860                
2861                  Rodrı́guez, Alexander,
2862                
2863              
2864            
2865          
2866        
2867          
2868          
2869          
2870          
2871            
2872              
2873                
2874                
2875          
2876          
2877          
2878            
2879              
2880                
2881                  <a href="https://chaozhang.org" target="_blank" rel="noopener noreferrer">Zhang, Chao</a>,
2882                
2883              
2884            
2885          
2886        
2887          
2888          
2889          
2890          
2891            
2892              
2893                
2894                
2895          
2896          
2897          
2898            
2899              
2900                
2901                  and <a href="https://cc.gatech.edu/~badityap" target="_blank" rel="noopener noreferrer">Prakash, B Aditya</a>
2902                
2903              
2904            
2905          
2906        
2907      </div>
2908
2909      <div class="periodical">
2910      
2911        <em>ACM Web Conference (WWW)</em>
2912      
2913      
2914        2022
2915      
2916      </div>
2917    
2918
2919    <div class="links">
2920    
2921      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
2922    
2923    
2924    
2925        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
2926    
2927    
2928    
2929      
2930      <a href="https://arxiv.org/abs/2109.07438" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
2931      
2932    
2933    
2934    
2935    
2936      <a href="https://github.com/AdityaLab/CAMul" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">
2936Code</a>
2937    
2938    
2939    
2940    
2941    </div>
2942
2943    <!-- Hidden abstract block -->
2944    
2945    <div class="abstract hidden">
2946      <p>Probabilistic time-series forecasting enables reliable decision making across many domains. Most forecasting problems have diverse sources of data containing multiple modalities and structures. Leveraging information as well as uncertainty from these data sources for well-calibrated and accurate forecasts is an important challenging problem. Most previous work on multi-modal learning and forecasting simply aggregate intermediate representations from each data view by simple methods of summation or concatenation and do not explicitly model uncertainty for each data-view. We propose a general probabilistic multi-view forecasting framework CAMul, that can learn representations and uncertainty from diverse data sources. It integrates the knowledge and uncertainty from each data view in a dynamic context-specific manner assigning more importance to useful views to model a well-calibrated forecast distribution. We use CAMul for multiple domains with varied sources and modalities and show that CAMul outperforms other state-of-art probabilistic forecasting models by over 25% in accuracy and calibration.</p>
2947    </div>
2948    
2949
2950    <!-- Hidden bibtex block -->
2951    
2952    <div class="bibtex hidden">
2953      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">kamarthi2021camul</span><span class="p">,</span>
2954  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{camul21}</span><span class="p">,</span>
2955  <span class="na">title</span> <span class="p">=</span> <span class="s">{CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting}</span><span class="p">,</span>
2956  <span class="na">author</span> <span class="p">=</span> <span class="s">{Kamarthi, Harshavardhan and Kong, Lingkai and Rodr{\'\i}guez, Alexander and Zhang, Chao and Prakash, B Aditya}</span><span class="p">,</span>
2957  <span class="na">journal</span> <span class="p">=</span> <span class="s">{ACM Web Conference (WWW)}</span><span class="p">,</span>
2958  <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span class="p">,</span>
2959  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
2960  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2109.07438}</span><span class="p">,</span>
2961  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
2962  <span class="na">code</span> <span class="p">=</span> <span class="s">{https://github.com/AdityaLab/CAMul}</span>
2963<span class="p">}</span></code></pre></figure>
2964    </div>
2965    
2966  </div>
2967</div>
2968</li>
2969<li>
2970<div class="row">
2971  <div class="col-sm-2 abbr">
2972  
2973    <img class="img-fluid" src="/assets/pubimg/back2future21.png" alt="back2future21">
2974  
2975  </div>
2976
2977  <div id="kamarthi2021back2future" class="col-sm-8">
2978    
2979      <div class="title">Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future</div>
2980      <div class="author">
2981        
2982          
2983          
2984          
2985          
2986          
2987          
2988            
2989              
2990                <em>Kamarthi, Harshavardhan</em>,
2991              
2992            
2993          
2994        
2995          
2996          
2997          
2998          
2999          
3000          
3001            
3002              
3003                
3004                  Rodrı́guez, Alexander,
3005                
3006              
3007            
3008          
3009        
3010          
3011          
3012          
3013          
3014            
3015              
3016                
3017                
3018          
3019          
3020          
3021            
3022              
3023                
3024                  and <a href="https://cc.gatech.edu/~badityap" target="_blank" rel="noopener noreferrer">Prakash, B Aditya</a>
3025                
3026              
3027            
3028          
3029        
3030      </div>
3031
3032      <div class="periodical">
3033      
3034        <em>ICLR</em>
3035      
3036      
3037        2022
3038      
3039      </div>
3040    
3041
3042    <div class="links">
3043    
3044      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
3045    
3046    
3047    
3048        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
3049    
3050    
3051    
3052      
3053      <a href="https://arxiv.org/abs/2106.04420" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
3054      
3055    
3056    
3057    
3058    
3059      <a href="https://github.com/AdityaLab/Back2Future" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">
3059Code</a>
3060    
3061    
3062    
3063    
3064    </div>
3065
3066    <!-- Hidden abstract block -->
3067    
3068    <div class="abstract hidden">
3069      <p>In real-time forecasting in public health, data collection is a non-trivial and demanding task. Often after initially released, it undergoes several revisions later (maybe due to human or technical constraints) - as a result, it may take weeks until the data reaches to a stable value. This so-called ’backfill’ phenomenon and its effect on model performance has been barely studied in the prior literature. In this paper, we introduce the multi-variate backfill problem using COVID-19 as the motivating example. We construct a detailed dataset composed of relevant signals over the past year of the pandemic. We then systematically characterize several patterns in backfill dynamics and leverage our observations for formulating a novel problem and neural framework Back2Future that aims to refines a given model’s predictions in real-time. Our extensive experiments demonstrate that our method refines the performance of top models for COVID-19 forecasting, in contrast to non-trivial baselines, yielding 18% improvement over baselines, enabling us obtain a new SOTA performance. In addition, we show that our model improves model evaluation too; hence policy-makers can better understand the true accuracy of forecasting models in real-time.</p>
3070    </div>
3071    
3072
3073    <!-- Hidden bibtex block -->
3074    
3075    <div class="bibtex hidden">
3076      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">kamarthi2021back2future</span><span class="p">,</span>
3077  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{back2future21}</span><span class="p">,</span>
3078  <span class="na">title</span> <span class="p">=</span> <span class="s">{Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future}</span><span class="p">,</span>
3079  <span class="na">author</span> <span class="p">=</span> <span class="s">{Kamarthi, Harshavardhan and Rodr{\'\i}guez, Alexander and Prakash, B Aditya}</span><span class="p">,</span>
3080  <span class="na">journal</span> <span class="p">=</span> <span class="s">{ICLR}</span><span class="p">,</span>
3081  <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span class="p">,</span>
3082  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
3083  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2106.04420}</span><span class="p">,</span>
3084  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
3085  <span class="na">code</span> <span class="p">=</span> <span class="s">{https://github.com/AdityaLab/Back2Future}</span>
3086<span class="p">}</span></code></pre></figure>
3087    </div>
3088    
3089  </div>
3090</div>
3091</li>
3092<li>
3093<div class="row">
3094  <div class="col-sm-2 abbr">
3095  
3096    <img class="img-fluid" src="/assets/pubimg/epifnp21.png" alt="epifnp21">
3097  
3098  </div>
3099
3100  <div id="kamarthi2021doubt" class="col-sm-8">
3101    
3102      <div class="title">When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting</div>
3103      <div class="author">
3104        
3105          
3106          
3107          
3108          
3109          
3110          
3111            
3112              
3113                <em>Kamarthi, Harshavardhan</em>,
3114              
3115            
3116          
3117        
3118          
3119          
3120          
3121          
3122            
3123              
3124                
3125                
3126          
3127          
3128          
3129            
3130              
3131                
3132                  <a href="https://lingkai-kong.com" target="_blank" rel="noopener noreferrer">Kong, Lingkai</a>,
3133                
3134              
3135            
3136          
3137        
3138          
3139          
3140          
3141          
3142          
3143          
3144            
3145              
3146                
3147                  Rodrı́guez, Alexander,
3148                
3149              
3150            
3151          
3152        
3153          
3154          
3155          
3156          
3157            
3158              
3159                
3160                
3161          
3162          
3163          
3164            
3165              
3166                
3167                  <a href="https://chaozhang.org" target="_blank" rel="noopener noreferrer">Zhang, Chao</a>,
3168                
3169              
3170            
3171          
3172        
3173          
3174          
3175          
3176          
3177            
3178              
3179                
3180                
3181          
3182          
3183          
3184            
3185              
3186                
3187                  and <a href="https://cc.gatech.edu/~badityap" target="_blank" rel="noopener noreferrer">Prakash, B Aditya</a>
3188                
3189              
3190            
3191          
3192        
3193      </div>
3194
3195      <div class="periodical">
3196      
3197        <em>Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS)</em>
3198      
3199      
3200        2021
3201      
3202      </div>
3203    
3204
3205    <div class="links">
3206    
3207      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
3208    
3209    
3210    
3211        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
3212    
3213    
3214    
3215      
3216      <a href="https://arxiv.org/abs/2106.03904" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
3217      
3218    
3219    
3220    
3221    
3222      <a href="https://github.com/AdityaLab/EpiFNP" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">
3222Code</a>
3223    
3224    
3225    
3226    
3227    </div>
3228
3229    <!-- Hidden abstract block -->
3230    
3231    <div class="abstract hidden">
3232      <p>Accurate and trustworthy epidemic forecasting is an important problem that has impact on public health planning and disease mitigation. Most existing epidemic forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions. Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations; e.g. it is difficult to specify meaningful priors in Bayesian NNs, while methods like deep ensembling are computationally expensive in practice. In this paper, we fill this important gap. We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP, which directly models the probability density of the forecast value. EPIFNP leverages a dynamic stochastic correlation graph to model the correlations between sequences in a non-parametric way, and designs different stochastic latent variables to capture functional uncertainty from different perspectives. Our extensive experiments in a real-time flu forecasting setting show that EPIFNP significantly outperforms previous state-of-the-art models in both accuracy and calibration metrics, up to 2.5x in accuracy and 2.4x in calibration. Additionally, due to properties of its generative process,EPIFNP learns the relations between the current season and similar patterns of historical seasons,enabling interpretable forecasts. Beyond epidemic forecasting, the EPIFNP can be of independent interest for advancing principled uncertainty quantification in deep sequential models for predictive analytics.</p>
3233    </div>
3234    
3235
3236    <!-- Hidden bibtex block -->
3237    
3238    <div class="bibtex hidden">
3239      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">kamarthi2021doubt</span><span class="p">,</span>
3240  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{epifnp21}</span><span class="p">,</span>
3241  <span class="na">title</span> <span class="p">=</span> <span class="s">{When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting}</span><span class="p">,</span>
3242  <span class="na">author</span> <span class="p">=</span> <span class="s">{Kamarthi, Harshavardhan and Kong, Lingkai and Rodr{\'\i}guez, Alexander and Zhang, Chao and Prakash, B Aditya}</span><span class="p">,</span>
3243  <span class="na">journal</span> <span class="p">=</span> <span class="s">{Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS)}</span><span class="p">,</span>
3244  <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span class="p">,</span>
3245  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
3246  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2106.03904}</span><span class="p">,</span>
3247  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
3248  <span class="na">code</span> <span class="p">=</span> <span class="s">{https://github.com/AdityaLab/EpiFNP}</span>
3249<span class="p">}</span></code></pre></figure>
3250    </div>
3251    
3252  </div>
3253</div>
3254</li>
3255<li>
3256<div class="row">
3257  <div class="col-sm-2 abbr">
3258  
3259    <img class="img-fluid" src="/assets/pubimg/selective20.png" alt="selective20">
3260  
3261  </div>
3262
3263  <div id="nishtala2021selective" class="col-sm-8">
3264    
3265      <div class="title">Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes</div>
3266      <div class="author">
3267        
3268          
3269          
3270          
3271          
3272          
3273          
3274            
3275              
3276                
3277                  Nishtala, Siddharth,
3278                
3279              
3280            
3281          
3282        
3283          
3284          
3285          
3286          
3287          
3288          
3289            
3290              
3291                
3292                  Madaan, Lovish,
3293                
3294              
3295            
3296          
3297        
3298          
3299          
3300          
3301          
3302          
3303          
3304            
3305              
3306                
3307                  Mate, Aditya,
3308                
3309              
3310            
3311          
3312        
3313          
3314          
3315          
3316          
3317          
3318          
3319            
3320              
3321                <em>Kamarthi, Harshavardhan</em>,
3322              
3323            
3324          
3325        
3326          
3327          
3328          
3329          
3330          
3331          
3332            
3333              
3334                
3335                  Grama, Anirudh,
3336                
3337              
3338            
3339          
3340        
3341          
3342          
3343          
3344          
3345          
3346          
3347            
3348              
3349                
3350                  Thakkar, Divy,
3351                
3352              
3353            
3354          
3355        
3356          
3357          
3358          
3359          
3360          
3361          
3362            
3363              
3364                
3365                  Narayanan, Dhyanesh,
3366                
3367              
3368            
3369          
3370        
3371          
3372          
3373          
3374          
3375          
3376          
3377            
3378              
3379                
3380                  Chaudhary, Suresh,
3381                
3382              
3383            
3384          
3385        
3386          
3387          
3388          
3389          
3390          
3391          
3392            
3393              
3394                
3395                  Madhiwalla, Neha,
3396                
3397              
3398            
3399          
3400        
3401          
3402          
3403          
3404          
3405          
3406          
3407            
3408              
3409                
3410                  Padmanabhan, Ramesh,
3411                
3412              
3413            
3414          
3415        
3416          
3417          
3418          
3419          
3420          
3421          
3422            
3423              
3424                
3425                  and others, 
3426                
3427              
3428            
3429          
3430        
3431      </div>
3432
3433      <div class="periodical">
3434      
3435        <em>arXiv preprint arXiv:2103.09052</em>
3436      
3437      
3438        2021
3439      
3440      </div>
3441    
3442
3443    <div class="links">
3444    
3445      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
3446    
3447    
3448    
3449        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
3450    
3451    
3452    
3453      
3454      <a href="https://arxiv.org/abs/2103.09052" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
3455      
3456    
3457    
3458    
3459    
3460    
3461    
3462    
3463    </div>
3464
3465    <!-- Hidden abstract block -->
3466    
3467    <div class="abstract hidden">
3468      <p>India has a maternal mortality ratio of 113 and child mortality ratio of 2830 per 100,000 live births. Lack of access to preventive care information is a major contributing factor for these deaths, especially in low resource households. We partner with ARMMAN, a non-profit based in India employing a call-based information program to disseminate health-related information to pregnant women and women with recent child deliveries. We analyze call records of over 300,000 women registered in the program created by ARMMAN and try to identify women who might not engage with these call programs that are proven to result in positive health outcomes. We built machine learning based models to predict the long term engagement pattern from call logs and beneficiaries’ demographic information, and discuss the applicability of this method in the real world through a pilot validation. Through a randomized controlled trial, we show that using our model’s predictions to make interventions boosts engagement metrics by 61.37%. We then formulate the intervention planning problem as restless multi-armed bandits (RMABs), and present preliminary results using this approach.</p>
3469    </div>
3470    
3471
3472    <!-- Hidden bibtex block -->
3473    
3474    <div class="bibtex hidden">
3475      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">nishtala2021selective</span><span class="p">,</span>
3476  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{selective20}</span><span class="p">,</span>
3477  <span class="na">title</span> <span class="p">=</span> <span class="s">{Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes}</span><span class="p">,</span>
3478  <span class="na">author</span> <span class="p">=</span> <span class="s">{Nishtala, Siddharth and Madaan, Lovish and Mate, Aditya and Kamarthi, Harshavardhan and Grama, Anirudh and Thakkar, Divy and Narayanan, Dhyanesh and Chaudhary, Suresh and Madhiwalla, Neha and Padmanabhan, Ramesh and others}</span><span class="p">,</span>
3479  <span class="na">
3479journal</span> <span class="p">=</span> <span class="s">{arXiv preprint arXiv:2103.09052}</span><span class="p">,</span>
3480  <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span class="p">,</span>
3481  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2103.09052}</span><span class="p">,</span>
3482  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
3483  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span>
3484<span class="p">}</span></code></pre></figure>
3485    </div>
3486    
3487  </div>
3488</div>
3489</li>
3490<li>
3491<div class="row">
3492  <div class="col-sm-2 abbr">
3493  
3494    <img class="img-fluid" src="/assets/pubimg/patrol20.png" alt="patrol20">
3495  
3496  </div>
3497
3498  <div id="venugopal2020reinforcement" class="col-sm-8">
3499    
3500      <div class="title">Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty</div>
3501      <div class="author">
3502        
3503          
3504          
3505          
3506          
3507          
3508          
3509            
3510              
3511                
3512                  Venugopal, Aravind,
3513                
3514              
3515            
3516          
3517        
3518          
3519          
3520          
3521          
3522            
3523              
3524                
3525                
3526          
3527          
3528          
3529            
3530              
3531                
3532                  <a href="https://sites.google.com/view/elizabethbondi" target="_blank" rel="noopener noreferrer">Bondi, Elizabeth</a>,
3533                
3534              
3535            
3536          
3537        
3538          
3539          
3540          
3541          
3542          
3543          
3544            
3545              
3546                <em>Kamarthi, Harshavardhan</em>,
3547              
3548            
3549          
3550        
3551          
3552          
3553          
3554          
3555          
3556          
3557            
3558              
3559                
3560                  Dholakia, Keval,
3561                
3562              
3563            
3564          
3565        
3566          
3567          
3568          
3569          
3570            
3571              
3572                
3573                
3574          
3575          
3576          
3577            
3578              
3579                
3580                  <a href="https://www.cse.iitm.ac.in/~ravi/" target="_blank" rel="noopener noreferrer">Ravindran, Balaraman</a>,
3581                
3582              
3583            
3584          
3585        
3586          
3587          
3588          
3589          
3590            
3591              
3592                
3593                
3594          
3595          
3596          
3597            
3598              
3599                
3600                  and <a href="https://teamcore.seas.harvard.edu/tambe" target="_blank" rel="noopener noreferrer">Tambe, Milind</a>
3601                
3602              
3603            
3604          
3605        
3606      </div>
3607
3608      <div class="periodical">
3609      
3610        <em>20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)</em>
3611      
3612      
3613        2020
3614      
3615      </div>
3616    
3617
3618    <div class="links">
3619    
3620      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
3621    
3622    
3623    
3624        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
3625    
3626    
3627    
3628      
3629      <a href="https://arxiv.org/abs/2012.10389" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
3630      
3631    
3632    
3633    
3634    
3635    
3636    
3637    
3638    </div>
3639
3640    <!-- Hidden abstract block -->
3641    
3642    <div class="abstract hidden">
3643      <p>Green Security Games (GSGs) have been successfully used in the protection of valuable resources such as fisheries, forests and wildlife. While real-world deployment involves both resource allocation and subsequent coordinated patrolling with communication and real-time, uncertain information, previous game models do not fully address both of these stages simultaneously. Furthermore, adopting existing solution strategies is difficult since they do not scale well for larger, more complex variants of the game models.
3644                 We therefore first propose a novel GSG model that combines defender allocation, patrolling, real-time drone notification to human patrollers, and drones sending warning signals to attackers. The model further incorporates uncertainty for real-time decision-making within a team of drones and human patrollers. Second, we present CombSGPO, a novel and scalable algorithm based on reinforcement learning, to compute a defender strategy for this game model. CombSGPO performs policy search over a multi-dimensional, discrete action space to compute an allocation strategy that is best suited to a best-response patrolling strategy for the defender, learnt by training a multi-agent Deep Q-Network. We show via experiments that CombSGPO converges to better strategies and is more scalable than comparable approaches. Third, we provide a detailed analysis of the coordination and signaling behavior learnt by CombSGPO, showing group formation between defender resources and patrolling formations based on signaling and notifications between resources. Importantly, we find that strategic signaling emerges in the final learnt strategy. Finally, we perform experiments to evaluate these strategies under different levels of uncertainty.</p>
3645    </div>
3646    
3647
3648    <!-- Hidden bibtex block -->
3649    
3650    <div class="bibtex hidden">
3651      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">venugopal2020reinforcement</span><span class="p">,</span>
3652  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{patrol20}</span><span class="p">,</span>
3653  <span class="na">title</span> <span class="p">=</span> <span class="s">{Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty}</span><span class="p">,</span>
3654  <span class="na">author</span> <span class="p">=</span> <span class="s">{Venugopal, Aravind and Bondi, Elizabeth and Kamarthi, Harshavardhan and Dholakia, Keval and Ravindran, Balaraman and Tambe, Milind}</span><span class="p">,</span>
3655  <span class="na">
3655journal</span> <span class="p">=</span> <span class="s">{20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)}</span><span class="p">,</span>
3656  <span class="na">year</span> <span class="p">=</span> <span class="s">{2020}</span><span class="p">,</span>
3657  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2012.10389}</span><span class="p">,</span>
3658  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
3659  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span>
3660<span class="p">}</span></code></pre></figure>
3661    </div>
3662    
3663  </div>
3664</div>
3665</li>
3666<li>
3667<div class="row">
3668  <div class="col-sm-2 abbr">
3669  
3670    <img class="img-fluid" src="/assets/pubimg/missedcall20.png" alt="missedcall20">
3671  
3672  </div>
3673
3674  <div id="nishtala2020missed" class="col-sm-8">
3675    
3676      <div class="title">Missed calls, Automated Calls and Health Support: Using AI to improve maternal health outcomes by increasing program engagement</div>
3677      <div class="author">
3678        
3679          
3680          
3681          
3682          
3683          
3684          
3685            
3686              
3687                
3688                  Nishtala, Siddharth,
3689                
3690              
3691            
3692          
3693        
3694          
3695          
3696          
3697          
3698          
3699          
3700            
3701              
3702                <em>Kamarthi, Harshavardhan</em>,
3703              
3704            
3705          
3706        
3707          
3708          
3709          
3710          
3711          
3712          
3713            
3714              
3715                
3716                  Thakkar, Divy,
3717                
3718              
3719            
3720          
3721        
3722          
3723          
3724          
3725          
3726          
3727          
3728            
3729              
3730                
3731                  Narayanan, Dhyanesh,
3732                
3733              
3734            
3735          
3736        
3737          
3738          
3739          
3740          
3741          
3742          
3743            
3744              
3745                
3746                  Grama, Anirudh,
3747                
3748              
3749            
3750          
3751        
3752          
3753          
3754          
3755          
3756          
3757          
3758            
3759              
3760                
3761                  Hegde, Aparna,
3762                
3763              
3764            
3765          
3766        
3767          
3768          
3769          
3770          
3771          
3772          
3773            
3774              
3775                
3776                  Padmanabhan, Ramesh,
3777                
3778              
3779            
3780          
3781        
3782          
3783          
3784          
3785          
3786          
3787          
3788            
3789              
3790                
3791                  Madhiwalla, Neha,
3792                
3793              
3794            
3795          
3796        
3797          
3798          
3799          
3800          
3801          
3802          
3803            
3804              
3805                
3806                  Chaudhary, Suresh,
3807                
3808              
3809            
3810          
3811        
3812          
3813          
3814          
3815          
3816            
3817              
3818                
3819                
3820          
3821          
3822          
3823            
3824              
3825                
3826                  <a href="https://www.cse.iitm.ac.in/~ravi/" target="_blank" rel="noopener noreferrer">Ravindran, Balaraman</a>,
3827                
3828              
3829            
3830          
3831        
3832          
3833          
3834          
3835          
3836          
3837          
3838            
3839              
3840                
3841                  and others, 
3842                
3843              
3844            
3845          
3846        
3847      </div>
3848
3849      <div class="periodical">
3850      
3851        <em>Harvard CRCS Workshop on AI for Social Good</em>
3852      
3853      
3854        2020
3855      
3856      </div>
3857    
3858
3859    <div class="links">
3860    
3861      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
3862    
3863    
3864    
3865        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
3866    
3867    
3868    
3869      
3870      <a href="https://arxiv.org/abs/2006.07590" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
3871      
3872    
3873    
3874    
3875    
3876    
3877    
3878    
3879    </div>
3880
3881    <!-- Hidden abstract block -->
3882    
3883    <div class="abstract hidden">
3884      <p>India accounts for 11% of maternal deaths globally where a woman dies in childbirth every fifteen minutes. Lack of access to preventive care information is a significant problem contributing to high maternal morbidity and mortality numbers, especially in low-income households. We work with ARMMAN, a non-profit based in India, to further the use of call-based information programs by early-on identifying women who might not engage on these programs that are proven to affect health parameters positively.We analyzed anonymized call-records of over 300,000 women registered in an awareness program created by ARMMAN that uses cellphone calls to regularly disseminate health related information. We built robust deep learning based models to predict short term and long term dropout risk from call logs and beneficiaries’ demographic information. Our model performs 13% better than competitive baselines for short-term forecasting and 7% better for long term forecasting. We also discuss the applicability of this method in the real world through a pilot validation that uses our method to perform targeted interventions.</p>
3885    </div>
3886    
3887
3888    <!-- Hidden bibtex block -->
3889    
3890    <div class="bibtex hidden">
3891      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">nishtala2020missed</span><span class="p">,</span>
3892  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{missedcall20}</span><span class="p">,</span>
3893  <span class="na">title</span> <span class="p">=</span> <span class="s">{Missed calls, Automated Calls and Health Support: Using AI to improve maternal health outcomes by increasing program engagement}</span><span class="p">,</span>
3894  <span class="na">author</span> <span class="p">=</span> <span class="s">{Nishtala, Siddharth and Kamarthi, Harshavardhan and Thakkar, Divy and Narayanan, Dhyanesh and Grama, Anirudh and Hegde, Aparna and Padmanabhan, Ramesh and Madhiwalla, Neha and Chaudhary, Suresh and Ravindran, Balaraman and others}</span><span class="p">,</span>
3895  <span class="na">
3895journal</span> <span class="p">=</span> <span class="s">{Harvard CRCS Workshop on AI for Social Good}</span><span class="p">,</span>
3896  <span class="na">year</span> <span class="p">=</span> <span class="s">{2020}</span><span class="p">,</span>
3897  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
3898  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
3899  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2006.07590}</span>
3900<span class="p">}</span></code></pre></figure>
3901    </div>
3902    
3903  </div>
3904</div>
3905</li>
3906<li>
3907<div class="row">
3908  <div class="col-sm-2 abbr">
3909  
3910    <img class="img-fluid" src="/assets/pubimg/influence19.png" alt="influence19">
3911  
3912  </div>
3913
3914  <div id="kamarthi2019influence" class="col-sm-8">
3915    
3916      <div class="title">Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling</div>
3917      <div class="author">
3918        
3919          
3920          
3921          
3922          
3923          
3924          
3925            
3926              
3927                <em>Kamarthi, Harshavardhan</em>,
3928              
3929            
3930          
3931        
3932          
3933          
3934          
3935          
3936          
3937          
3938            
3939              
3940                
3941                  Vijayan, Priyesh,
3942                
3943              
3944            
3945          
3946        
3947          
3948          
3949          
3950          
3951          
3952          
3953            
3954              
3955                
3956                  Wilder, Bryan,
3957                
3958              
3959            
3960          
3961        
3962          
3963          
3964          
3965          
3966            
3967              
3968                
3969                
3970          
3971          
3972          
3973            
3974              
3975                
3976                  <a href="https://www.cse.iitm.ac.in/~ravi/" target="_blank" rel="noopener noreferrer">Ravindran, Balaraman</a>,
3977                
3978              
3979            
3980          
3981        
3982          
3983          
3984          
3985          
3986            
3987              
3988                
3989                
3990          
3991          
3992          
3993            
3994              
3995                
3996                  and <a href="https://teamcore.seas.harvard.edu/tambe" target="_blank" rel="noopener noreferrer">Tambe, Milind</a>
3997                
3998              
3999            
4000          
4001        
4002      </div>
4003
4004      <div class="periodical">
4005      
4006        <em>19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), <b>Nominated for Best Paper Award&lt;\b&gt; </b></em>
4007      
4008      
4009        2019
4010      
4011      </div>
4012    
4013
4014    <div class="links">
4015    
4016      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
4017    
4018    
4019    
4020        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
4021    
4022    
4023    
4024      
4025      <a href="https://arxiv.org/abs/1907.11625" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
4026      
4027    
4028    
4029    
4030    
4031      <a href="https://github.com/kage08/graph_sample_rl" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">Code</a>
4032    
4033    
4034    
4035    
4036    </div>
4037
4038    <!-- Hidden abstract block -->
4039    
4040    <div class="abstract hidden">
4041      <p>A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network. Current state-of-the-art methods rely on hand-crafted sampling algorithms; these methods sample nodes and their neighbours in a carefully constructed order and choose opinion leaders from this discovered network to maximize influence spread in the (unknown) complete network. In this work, we propose a reinforcement learning framework for network discovery that automatically learns useful node and graph representations that encode important structural properties of the network. At training time, the method identifies portions of the network such that the nodes selected from this sampled subgraph can effectively influence nodes in the complete network. The realization of such transferable network structure based adaptable policies is attributed to the meticulous design of the framework that encodes relevant node and graph signatures driven by an appropriate reward scheme. We experiment with real-world social networks from four different domains and show that the policies learned by our RL agent provide a 10-36% improvement over the current state-of-the-art method.</p>
4042    </div>
4043    
4044
4045    <!-- Hidden bibtex block -->
4046    
4047    <div class="bibtex hidden">
4048      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">kamarthi2019influence</span><span class="p">,</span>
4049  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{influence19}</span><span class="p">,</span>
4050  <span class="na">title</span> <span class="p">=</span> <span class="s">{Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling}</span><span class="p">,</span>
4051  <span class="na">author</span> <span class="p">=</span> <span class="s">{Kamarthi, Harshavardhan and Vijayan, Priyesh and Wilder, Bryan and Ravindran, Balaraman and Tambe, Milind}</span><span class="p">,</span>
4052  <span class="na">
4052journal</span> <span class="p">=</span> <span class="s">{19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), &lt;b&gt;Nominated for Best Paper Award&lt;\b&gt; }</span><span class="p">,</span>
4053  <span class="na">note</span> <span class="p">=</span> <span class="s">{Nominated for best paper award}</span><span class="p">,</span>
4054  <span class="na">year</span> <span class="p">=</span> <span class="s">{2019}</span><span class="p">,</span>
4055  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
4056  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/1907.11625}</span><span class="p">,</span>
4057  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
4058  <span class="na">code</span> <span class="p">=</span> <span class="s">{https://github.com/kage08/graph_sample_rl}</span>
4059<span class="p">}</span></code></pre></figure>
4060    </div>
4061    
4062  </div>
4063</div>
4064</li>
4065<li>
4066<div class="row">
4067  <div class="col-sm-2 abbr">
4068  
4069    <img class="img-fluid" src="/assets/pubimg/integrating19.png" alt="integrating19">
4070  
4071  </div>
4072
4073  <div id="srinivasan2019integrating" class="col-sm-8">
4074    
4075      <div class="title">Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting</div>
4076      <div class="author">
4077        
4078          
4079          
4080          
4081          
4082          
4083          
4084            
4085              
4086                
4087                  Srinivasan, Aakash,
4088                
4089              
4090            
4091          
4092        
4093          
4094          
4095          
4096          
4097          
4098          
4099            
4100              
4101                <em>Kamarthi, Harshavardhan</em>,
4102              
4103            
4104          
4105        
4106          
4107          
4108          
4109          
4110          
4111          
4112            
4113              
4114                
4115                  Ganesan, Devi,
4116                
4117              
4118            
4119          
4120        
4121          
4122          
4123          
4124          
4125          
4126          
4127            
4128              
4129                
4130                  and Chakraborti, Sutanu
4131                
4132              
4133            
4134          
4135        
4136      </div>
4137
4138      <div class="periodical">
4139      
4140        <em>International Conference on Natural Language Processing (ICNLP)</em>
4141      
4142      
4143        2019
4144      
4145      </div>
4146    
4147
4148    <div class="links">
4149    
4150      <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
4151    
4152    
4153    
4154        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
4155    
4156    
4157    
4158      
4159      <a href="https://arxiv.org/pdf/1912.06889" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
4160      
4161    
4162    
4163    
4164    
4165    
4166    
4167    
4168    </div>
4169
4170    <!-- Hidden abstract block -->
4171    
4172    <div class="abstract hidden">
4173      <p>Neural network based word embeddings, such as Word2Vec and GloVe, are purely data driven in that they capture the distributional information about words from the training corpus. Past works have attempted to improve these embeddings by incorporating semantic knowledge from lexical resources like WordNet. Some techniques like retrofitting modify word embeddings in the post-processing stage while some others use a joint learning approach by modifying the objective function of neural networks. In this paper, we discuss two novel approaches for incorporating semantic knowledge into word embeddings. In the first approach, we take advantage of Levy et al’s work which showed that using SVD based methods on co-occurrence matrix provide similar performance to neural network based embeddings. We propose a ’sprinkling’ technique to add semantic relations to the co-occurrence matrix directly before factorization. In the second approach, WordNet similarity scores are used to improve the retrofitting method. We evaluate the proposed methods in both intrinsic and extrinsic tasks and observe significant improvements over the baselines in many of the datasets.</p>
4174    </div>
4175    
4176
4177    <!-- Hidden bibtex block -->
4178    
4179    <div class="bibtex hidden">
4180      <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">srinivasan2019integrating</span><span class="p">,</span>
4181  <span class="na">abbr</span> <span class="p">=</span> <span class="s">{integrating19}</span><span class="p">,</span>
4182  <span class="na">title</span> <span class="p">=</span> <span class="s">{Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting}</span><span class="p">,</span>
4183  <span class="na">author</span> <span class="p">=</span> <span class="s">{Srinivasan, Aakash and Kamarthi, Harshavardhan and Ganesan, Devi and Chakraborti, Sutanu}</span><span class="p">,</span>
4184  <span class="na">
4184journal</span> <span class="p">=</span> <span class="s">{International Conference on Natural Language Processing (ICNLP)}</span><span class="p">,</span>
4185  <span class="na">year</span> <span class="p">=</span> <span class="s">{2019}</span><span class="p">,</span>
4186  <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span class="p">,</span>
4187  <span class="na">pdf</span> <span class="p">=</span> <span class="s">{https://arxiv.org/pdf/1912.06889}</span><span class="p">,</span>
4188  <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span>
4189<span class="p">}</span></code></pre></figure>
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