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1<table><thead><tr><th>2025</th><th> </th></tr></thead><tbody><tr><td><a href="https://neurips.cc/virtual/2025/poster/115391">pdf</a></td><td>Yongxin He, Shan Zhang, Yixuan Cao, Lei Ma, Ping Luo. DETree: DEtecting Human-AI Collaborative Texts via Tree-Structured Hierarchical Representation Learning. In NeurIPS, 2025.</td></tr><tr><td><a href="https://openreview.net/forum?id=x0tdNRfQhe">pdf</a></td><td>Lvzhou Luo, Yixuan Cao, Ping Luo. AttnComp: Attention-Guided Adaptive Context Compression for Retrieval-Augmented Generation. In EMNLP Findings, 2025.</td></tr><tr><td><a href="https://ping-luo.github.io/files/2025_ConciseExplain- Reducing Redundancy and Spuriousness in Persuasive Recommendation Explanation.pdf">pdf</a></td><td>Yixuan Cao, Juyao Liu, Haodong Wang, Jian Wang, Kun Wan, Gang Xiao, and Ping Luo. ConciseExplain: Reducing Redundancy and Spuriousness in Persuasive Recommendation Explanation. In KDD, 2025.</td></tr><tr><td><a href="https://ping-luo.github.io/files/2025_Attention with Dependency Parsing Augmentation for Fine-Grained Attribution.pdf">pdf</a></td><td>Qiang Ding, Lvzhou Luo, Yixuan Cao, Ping Luo. Attention with Dependency Parsing Augmentation for Fine-Grained Attribution. In ACL Findings, 2025.</td></tr><tr><td><a href="https://ping-luo.github.io/files/2025_Whether LLMs Know If They Know- Identifying Knowledge Boundaries via Debiased Historical In-Context Learning.pdf">pdf</a></td><td>Bo Lv, Nayu Liu, Yang Shen, Xin Liu, Ping Luo, Yue Yu. Whether LLMs Know If They Know: Identifying Knowledge Boundaries via Debiased Historical In-Context Learning. In ACL Findings, 2025.</td></tr><tr><td> </td><td>Hongshuo Chen, Yixuan Cao, Shiwei Ye, and Ping Luo. Tagging Generatively: Dynamic and Open-Ended Image Tagging with MLLM. In IJCNN, 2025.</td></tr><tr><td><a href="https://ping-luo.github.io/files/2025_Towards Efficient Quantity Retrieval from Text- An Approach via Description Parsing and Weak Supervision.pdf">pdf</a></td><td>Yixuan Cao, Zhengrong Chen, Chengxuan Xia, Kun Wu, and Ping Luo. Towards Efficient Quantity Retrieval from Text: An Approach via Description Parsing and Weak Supervision. In DEXA, 2025.</td></tr><tr><td><a href="https://link.springer.com/chapter/10.1007/978-3-032-02088-8_6">pdf</a></td><td>Yifan Liu, Yixuan Cao, and Ping Luo. Inspire Me with Your Questions: Repurposing Historical Questions for New Documents. In DEXA, 2025.</td></tr></tbody></table><table><thead><tr><th>2024</th><th> </th></tr></thead><tbody><tr><td><a href="https://ping-luo.github.io/files/2024_Uncovering Limitations of Large Language Models in Information Seeking from Tables.pdf">pdf</a></td><td>Chaoxu Pang, Yixuan Cao, Chunhao Yang, Ping Luo: Uncovering Limitations of Large Language Models in Information Seeking from Tables. ACL (Findings) 2024: 1388-1409</td></tr></tbody></table><table><thead><tr><th>2023</th><th> </th></tr></thead><tbody><tr><td><a href="https://papers.nips.cc/paper_files/paper/2023/file/6f7fa4df2c8a79c164d3697898a32bd9-Paper-Conference.pdf">pdf</a></td><td>Qiang Ding, Yixuan Cao, Ping Luo: Top-Ambiguity Samples Matter: Understanding Why Deep Ensemble Works in Selective Classification. NeurIPS 2023</td></tr><tr><td><a href="https://ping-luo.github.io/files/2023_Guideline Learning for In-Context Information Extraction.pdf">pdf</a></td><td>Chaoxu Pang, Yixuan Cao, Qiang Ding, Ping Luo: Guideline Learning for In-Context Information Extraction. EMNLP 2023: 15372-15389</td></tr></tbody></table><table><thead><tr><th>2022</th><th> </th></tr></thead><tbody><tr><td><a href="https://link.springer.com/article/10.1007/s11390-021-1076-7">pdf</a></td><td>Rongyu Cao, Yixuan Cao, Ganbin Zhou, Ping Luo: Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and Application. J. Comput. Sci. Technol. 37(3): 699-718 (2022)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2022_Numerical Tuple Extraction from Tables with Pre-training.pdf">pdf</a></td><td>Qingping Yang, Yixuan Cao, Ping Luo: Numerical Tuple Extraction from Tables with Pre-training. KDD 2022: 2233-2241</td></tr></tbody></table><table><thead><tr><th>
12021</th><th> </th></tr></thead><tbody><tr><td><a href="https://ping-luo.github.io/files/2020_Nested Relation Extraction with Iterative Neural Network.pdf">pdf</a></td><td>Yixuan Cao, Dian Chen, Zhengqi Xu, Hongwei Li, Ping Luo: Nested Relation Extraction with Iterative Neural Network. Frontiers Comput. Sci. 15(3): 153323 (2021)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2020_Rich-text Document Styling Restoration via Reinforcement Learning.pdf">pdf</a></td><td>Hongwei Li, Yingpeng Hu, Yixuan Cao, Ganbin Zhou, Ping Luo: Rich-text Document Styling Restoration via Reinforcement Learning. Frontiers Comput. Sci. (2021)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2021_A Bottom-Up DAG Structure Extraction Model for Math Word Problems.pdf">pdf</a></td><td>Yixuan Cao, Feng Hong, Hongwei Li, Ping Luo: A Bottom-Up DAG Structure Extraction Model for Math Word Problems. AAAI 2021: 39-46</td></tr><tr><td><a href="https://ping-luo.github.io/files/2021_Extracting Zero-shot Structured Information from Form-like Documents Pretraining with Keys and Triggers.pdf">pdf</a></td><td>Rongyu Cao, Ping Luo: Extracting Zero-shot Structured Information from Form-like Documents Pretraining with Keys and Triggers. AAAI 2021</td></tr><tr><td><a href="https://dl.acm.org/doi/10.1145/3459637.3481929">pdf</a></td><td>Zhengqi Xu, Yixuan Cao, Rongyu Cao, Guoxiang Li, Xuanqiang Liu, Yan Pang, Yangbin Wang, Jianfei Zhang, Allie Cheung, Matthew Tam, Lukas Petrikas, Ping Luo: Jura: Towards Automatic Compliance Assessment for Annual Reports of Listed Companies. CIKM 2021: 4264-4272</td></tr><tr><td><a href="https://link.springer.com/chapter/10.1007/978-3-030-86331-9_1">pdf</a></td><td>Rongyu Cao, Hongwei Li, Ganbin Zhou, Ping Luo: Towards Document Panoptic Segmentation with Pinpoint Accuracy: Method and Evaluation. ICDAR (2) 2021: 3-18</td></tr><tr><td><a href="https://ieeexplore.ieee.org/document/9679080/">pdf</a></td><td>Qingping Yang, Yingpeng Hu, Rongyu Cao, Hongwei Li, Ping Luo: Zero-shot Key Information Extraction from Mixed-Style Tables: Pre-training on Wikipedia. ICDM 2021: 1451-1456</td></tr><tr><td><a href="https://ping-luo.github.io/files/2021_Numerical Formula Recognition from Tables.pdf">pdf</a></td><td>Qingping Yang, Yixuan Cao, Hongwei Li, Ping Luo: Numerical Formula Recognition from Tables. KDD 2021: 1986-1996</td></tr></tbody></table><table><thead><tr><th>2020</th><th> </th></tr></thead><tbody><tr><td><a href="https://linkinghub.elsevier.com/retrieve/pii/S0306437919300365">pdf</a></td><td>Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He: Corrigendum to “Discovering and learning sensational episodes of news events”. Inf. Syst. 93: 101364 (2020)</td></tr><tr><td><a href="https://dl.acm.org/doi/10.1145/3364221">pdf</a></td><td>Ping Luo, Kai Shu, Junjie Wu, Li Wan, Yong Tan: Exploring Correlation Network for Cheating Detection. ACM Trans. Intell. Syst. Technol. 11(1): 12:1-12:23 (2020)</td></tr><tr><td><a href="https://doi.org/10.1007/978-3-030-59003-1_24">pdf</a></td><td>Hongwei Li, Qingping Yang, Yixuan Cao, Ganbin Zhou, Ping Luo: Semantic Matching over Matrix-Style Tables in Richly Formatted Documents. DEXA (1) 2020: 369-384</td></tr><tr><td><a href="https://ping-luo.github.io/files/2020_Cracking Tabular Presentation Diversity for Automatic Cross-Checking over Numerical Facts.pdf">pdf</a></td><td>Hongwei Li, Qingping Yang, Yixuan Cao, Jiaquan Yao, Ping Luo: Cracking Tabular Presentation Diversity for Automatic Cross-Checking over Numerical Facts. KDD 2020: 2599-2607</td></tr><tr><td><a href="https://ping-luo.github.io/files/2020_Pairwise Causality Structure Towards Nested Causality Mining on Financial Statements.pdf">pdf</a></td><td>Dian Chen, Yixuan Cao, Ping Luo: Pairwise Causality Structure Towards Nested Causality Mining on Financial Statements. NLPCC 2020: 725-737</td></tr></tbody></table><table><thead><tr><th>2019</th><th> </th></tr></thead><tbody><tr><td><a href="https://ping-luo.github.io/files/2019_Nested Relation Extraction with Iterative Neural Network.pdf">pdf</a></td><td>Yixuan Cao, Dian Chen, Hongwei Li, Ping Luo: Nested Relation Extraction with Iterative Neural Network. CIKM 2019: 1001-1010</td></tr></tbody></table><table><thead><tr><th>
12018</th><th> </th></tr></thead><tbody><tr><td><a href="https://ping-luo.github.io/files/2018_Predicting compositional time series via autoregressive Dirichlet estimation.pdf">pdf</a></td><td>Ganbin Zhou, Ping Luo, Qing He: Predicting compositional time series via autoregressive Dirichlet estimation. SCIENCE CHINA Information Sciences 61(9): 098106: 1-098106: 3 (2018)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2018_Discovering and learning sensational episodes of news events.pdf">pdf</a></td><td>Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He: Discovering and learning sensational episodes of news events. Inf. Syst. 78: 68-80 (2018)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2018_Supervised Representation Learning with Double Encoding-Layer Autoencoder for Transfer Learning.pdf">pdf</a></td><td>Fuzhen Zhuang, Xiaohu Cheng, Ping Luo, Sinno Jialin Pan, Qing He: Supervised Representation Learning with Double Encoding-Layer Autoencoder for Transfer Learning. ACM TIST 9(2): 16: 1-16: 17 (2018)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2018_Mining Precise-Positioning Episode Rules from Event Sequences.pdf">pdf</a></td><td>Xiang Ao, Ping Luo, Jin Wang, Fuzhen Zhuang, Qing He: Mining Precise-Positioning Episode Rules from Event Sequences. IEEE Trans. Knowl. Data Eng. 30(3): 530-543 (2018)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2018_Conversational Model Adaptation via KL Divergence Regularization.pdf">pdf</a></td><td>Juncen Li, Ping Luo, Fen Lin, Bo Chen: Conversational Model Adaptation via KL Divergence Regularization. AAAI 2018: 5213-5219</td></tr><tr><td><a href="https://ping-luo.github.io/files/2018_Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation.pdf">pdf</a></td><td>Ganbin Zhou, Ping Luo, Rongyu Cao, Yijun Xiao, Fen Lin, Bo Chen, Qing He: Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation. AAAI 2018: 5722-5729</td></tr><tr><td><a href="https://ping-luo.github.io/files/2018_Elastic Responding Machine for Dialog Generation with Dynamically Mechanism Selecting.pdf">pdf</a></td><td>Ganbin Zhou, Ping Luo, Yijun Xiao, Fen Lin, Bo Chen, Qing He: Elastic Responding Machine for Dialog Generation with Dynamically Mechanism Selecting. AAAI 2018: 5730-5737</td></tr><tr><td><a href="https://ping-luo.github.io/files/2018_Exploiting User Actions for App Recommendations.pdf">pdf</a></td><td>Kai Shu, Suhang Wang, Huan Liu, Jiliang Tang, Yi Chang, Ping Luo: Exploiting User Actions for App Recommendations. ASONAM 2018: 139-142</td></tr><tr><td><a href="https://ping-luo.github.io/files/2018_Adaboost with Auto-Evaluation for Conversational Models.pdf">pdf</a></td><td>Juncen Li, Ping Luo, Ganbin Zhou, Fen Lin, Cheng Niu: Adaboost with Auto-Evaluation for Conversational Models. IJCAI 2018: 4173-4179</td></tr><tr><td><a href="https://ping-luo.github.io/files/2018_Towards Automatic Numerical Cross-Checking Extracting Formulas from Text.pdf">pdf</a></td><td>Yixuan Cao, Hongwei Li, Ping Luo, Jiaquan Yao: Towards Automatic Numerical Cross-Checking: Extracting Formulas from Text. WWW 2018: 1795-1804</td></tr></tbody></table><table><thead><tr><th>2017</th><th> </th></tr></thead><tbody><tr><td><a href="https://ping-luo.github.io/files/2017_Discerning individual interests and shared interests for social user profiling.pdf">pdf</a></td><td>Enhong Chen, Guangxiang Zeng, Ping Luo, Hengshu Zhu, Jilei Tian, Hui Xiong: Discerning individual interests and shared interests for social user profiling. World Wide Web 20(2): 417-435 (2017)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2017_Mechanism-Aware Neural Machine for Dialogue Response Generation.pdf">pdf</a></td><td>Ganbin Zhou, Ping Luo, Rongyu Cao, Fen Lin, Bo Chen, Qing He: Mechanism-Aware Neural Machine for Dialogue Response Generation. AAAI 2017: 3400-3407</td></tr><tr><td><a href="https://ping-luo.github.io/files/2017_Mining Precise-Positioning Episode Rules from Event Sequences (Poster).pdf">pdf</a></td><td>Xiang Ao, Ping Luo, Jin Wang, Fuzhen Zhuang, Qing He: Mining Precise-Positioning Episode Rules from Event Sequences. ICDE 2017: 83-86</td></tr><tr><td><a href="https://ping-luo.github.io/files/2017_Small Batch or Large Batch Gaussian Walk with Rebound Can Teach.pdf">pdf</a></td><td>Peifeng Yin, Ping Luo, Taiga Nakamura: Small Batch or Large Batch?: Gaussian Walk with Rebound Can Teach. KDD 2017: 1275-1284</td></tr></tbody></table><table><thead><tr><th>
12016</th><th> </th></tr></thead><tbody><tr><td><a href="https://ping-luo.github.io/files/2016_Revisiting bound estimation of pattern measures A generic framework.pdf">pdf</a></td><td>Lei Zhang, Ping Luo, Enhong Chen, Min Wang: Revisiting bound estimation of pattern measures: A generic framework. Inf. Sci. 339: 254-273 (2016)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2016_Ensemble of Anchor Adapters for Transfer Learning.pdf">pdf</a></td><td>Fuzhen Zhuang, Ping Luo, Sinno Jialin Pan, Hui Xiong, Qing He: Ensemble of Anchor Adapters for Transfer Learning. CIKM 2016: 2335-2340</td></tr><tr><td><a href="https://ping-luo.github.io/files/2016_Browsing Regularities in Hedonic Content Systems.pdf">pdf</a></td><td>Ping Luo, Ganbin Zhou, Jiaxi Tang, Rui Chen, Zhongjie Yu, Qing He: Browsing Regularities in Hedonic Content Systems. IJCAI 2016: 3811-3817</td></tr><tr><td><a href="https://ping-luo.github.io/files/2016_From Online Behaviors to Offline Retailing.pdf">pdf</a></td><td>Ping Luo, Su Yan, Zhiqiang Liu, Zhiyong Shen, Shengwen Yang, Qing He: From Online Behaviors to Offline Retailing. KDD 2016: 175-184</td></tr></tbody></table><table><thead><tr><th>2015</th><th> </th></tr></thead><tbody><tr><td><a href="https://ping-luo.github.io/files/2015_A Hybrid Framework for Semantic Relation Extraction over Enterprise Data.pdf">pdf</a></td><td>Wei Shen, Jianyong Wang, Ping Luo, Min Wang: A Hybrid Framework for Semantic Relation Extraction over Enterprise Data. Int. J. Semantic Web Inf. Syst. 11(3): 1-24 (2015)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2015_Occupancy-Based Frequent Pattern Mining.pdf">pdf</a></td><td>Lei Zhang, Ping Luo, Linpeng Tang, Enhong Chen, Qi Liu, Min Wang, Hui Xiong: Occupancy-Based Frequent Pattern Mining*. TKDD 10(2): 14: 1-14: 33 (2015)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2015_Collaborating between Local and Global Learning for Distributed Online Multiple Tasks.pdf">pdf</a></td><td>Xin Jin, Ping Luo, Fuzhen Zhuang, Jia He, Qing He: Collaborating between Local and Global Learning for Distributed Online Multiple Tasks. CIKM 2015: 113-122</td></tr><tr><td><a href="https://ping-luo.github.io/files/2015_Heterogeneous Multi-task Semantic Feature Learning for Classification.pdf">pdf</a></td><td>Xin Jin, Fuzhen Zhuang, Sinno Jialin Pan, Changying Du, Ping Luo, Qing He: Heterogeneous Multi-task Semantic Feature Learning for Classification. CIKM 2015: 1847-1850</td></tr><tr><td><a href="https://ping-luo.github.io/files/2015_Online Frequent Episode Mining.pdf">pdf</a></td><td>Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He: Online Frequent Episode Mining. ICDE 2015: 891-902</td></tr><tr><td><a href="https://ping-luo.github.io/files/2015_Max-Intensity Detecting Competitive Advertiser Communities in Sponsored Search Market.pdf">pdf</a></td><td>Wenchao Yu, Ariyam Das, Justin Wood, Wei Wang, Carlo Zaniolo, Ping Luo: Max-Intensity: Detecting Competitive Advertiser Communities in Sponsored Search Market. ICDM 2015: 569-578</td></tr><tr><td><a href="https://ping-luo.github.io/files/2015_Representation Learning via Semi-Supervised Autoencoder for Multi-task Learning.pdf">pdf</a></td><td>Fuzhen Zhuang, Dan Luo, Xin Jin, Hui Xiong, Ping Luo, Qing He: Representation Learning via Semi-Supervised Autoencoder for Multi-task Learning. ICDM 2015: 1141-1146</td></tr><tr><td><a href="https://ping-luo.github.io/files/2015_Matrix Factorization with Scale-Invariant Parameters.pdf">pdf</a></td><td>Guangxiang Zeng, Hengshu Zhu, Qi Liu, Ping Luo, Enhong Chen, Tong Zhang: Matrix Factorization with Scale-Invariant Parameters. IJCAI 2015: 4017-4024</td></tr><tr><td><a href="https://ping-luo.github.io/files/2015_Supervised Representation Learning Transfer Learning with Deep Autoencoders.pdf">pdf</a></td><td>Fuzhen Zhuang, Xiaohu Cheng, Ping Luo, Sinno Jialin Pan, Qing He: Supervised Representation Learning: Transfer Learning with Deep Autoencoders. IJCAI 2015: 4119-4125</td></tr><tr><td><a href="https://ping-luo.github.io/files/2015_Convex Matrix Completion A Trace-Ball Optimization Perspective.pdf">pdf</a></td><td>Guangxiang Zeng, Ping Luo, Enhong Chen, Hui Xiong, Hengshu Zhu, Qi Liu: Convex Matrix Completion: A Trace-Ball Optimization Perspective. SDM 2015: 334-342</td></tr><tr><td><a href="https://ping-luo.github.io/files/2015_Festival, Date and Limit Line Predicting Vehicle Accident Rate in Beijing.pdf">pdf</a></td><td>Xinyu Wu, Ping Luo, Qing He, Tianshu Feng, Fuzhen Zhuang: Festival, Date and Limit Line: Predicting Vehicle Accident Rate in Beijing. SDM 2015: 945-953</td></tr></tbody></table><table><thead><tr><th>
12014</th><th> </th></tr></thead><tbody><tr><td><a href="https://ping-luo.github.io/files/2014_Efficient incremental update and querying in AWETO RDF storage system.pdf">pdf</a></td><td>Xu Pu, Jianyong Wang, Zhenhua Song, Ping Luo, Min Wang: Efficient incremental update and querying in AWETO RDF storage system. Data Knowl. Eng. 89: 55-75 (2014)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2014_Combining supervised and unsupervised models via unconstrained probabilistic embedding.pdf">pdf</a></td><td>Xiang Ao, Ping Luo, Xudong Ma, Fuzhen Zhuang, Qing He, Zhongzhi Shi, Zhiyong Shen: Combining supervised and unsupervised models via unconstrained probabilistic embedding. Inf. Sci. 257: 101-114 (2014)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2014_Toward detection of aliases without string similarity.pdf">pdf</a></td><td>Ning An, Lili Jiang, Jianyong Wang, Ping Luo, Min Wang, Bing Nan Li: Toward detection of aliases without string similarity. Inf. Sci. 261: 89-100 (2014)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2014_Triplex Transfer Learning Exploiting Both Shared and Distinct Concepts for Text Classification.pdf">pdf</a></td><td>Fuzhen Zhuang, Ping Luo, Changying Du, Qing He, Zhongzhi Shi, Hui Xiong: Triplex Transfer Learning: Exploiting Both Shared and Distinct Concepts for Text Classification. IEEE Trans. Cybernetics 44(7): 1191-1203 (2014)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2014_Multi-task Multi-view Learning for Heterogeneous Tasks.pdf">pdf</a></td><td>Xin Jin, Fuzhen Zhuang, Hui Xiong, Changying Du, Ping Luo, Qing He: Multi-task Multi-view Learning for Heterogeneous Tasks. CIKM 2014: 441-450</td></tr><tr><td><a href="https://ping-luo.github.io/files/2014_Deal or deceit detecting cheating in distribution channels.pdf">pdf</a></td><td>Kai Shu, Ping Luo, Wan Li, Peifeng Yin, Linpeng Tang: Deal or deceit: detecting cheating in distribution channels. CIKM 2014: 1419-1428</td></tr><tr><td><a href="https://ping-luo.github.io/files/2014_Mining user tasks from print logs.pdf">pdf</a></td><td>Xin Li, Lei Zhang, Ping Luo, Enhong Chen, Guandong Xu, Yu Zong, Chu Guan: Mining user tasks from print logs. IJCNN 2014: 1250-1257</td></tr><tr><td><a href="https://ping-luo.github.io/files/2014_Discovering and learning sensational episodes of news events.pdf">pdf</a></td><td>Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He, Zhongzhi Shi: Discovering and learning sensational episodes of news events. WWW (Companion Volume) 2014: 217-218</td></tr></tbody></table><table><thead><tr><th>2013</th><th> </th></tr></thead><tbody><tr><td><a href="https://ping-luo.github.io/files/2013_Discovering General Prominent Streaks in Sequence Data.pdf">pdf</a></td><td>Gensheng Zhang, Xiao Jiang, Ping Luo, Min Wang, Chengkai Li: Discovering General Prominent Streaks in Sequence Data. TKDD 8(2): 9:1-9:37 (2013)</td></tr><tr><td>pdf</td><td>Lili Jiang, Ping Luo, Jianyong Wang, Yuhong Xiong, Bingduan Lin, Min Wang, Ning An: GRIAS: An Entity-Relation Graph Based Framework for Discovering Entity Aliases. ICDM 2013: 310-319</td></tr><tr><td><a href="https://ping-luo.github.io/files/2013_From Social User Activities to People Affiliation.pdf">pdf</a></td><td>Guangxiang Zeng, Ping Luo, Enhong Chen, Min Wang: From Social User Activities to People Affiliation. ICDM 2013: 1277-1282</td></tr><tr><td><a href="https://ping-luo.github.io/files/2013_Concept Learning for Crossdomain Text Classification a General Probabilistic Framework.pdf">pdf</a></td><td>Fuzhen Zhuang, Ping Luo, Peifeng Yin, Qing He, Zhongzhi Shi: Concept Learning for Cross-Domain Text Classification: A General Probabilistic Framework. IJCAI 2013: 1960-1967</td></tr><tr><td><a href="https://ping-luo.github.io/files/2013_Linking Named Entities in Tweets with Knowledge Base via User Interest Modeling.pdf">pdf</a></td><td>Wei Shen, Jianyong Wang, Ping Luo, Min Wang: Linking named entities in Tweets with knowledge base via user interest modeling. KDD 2013: 68-76</td></tr><tr><td><a href="https://ping-luo.github.io/files/2013_Silence is Also Evidence_Interpreting Dwell Time for Recommendation from_Psychological Perspective.pdf">pdf</a></td><td>Peifeng Yin, Ping Luo, Wang-Chien Lee, Min Wang: Silence is also evidence: interpreting dwell time for recommendation from psychological perspective. KDD 2013: 989-997</td></tr><tr><td><a href="https://ping-luo.github.io/files/2013_Embedding with Autoencoder Regularization.pdf">pdf</a></td><td>Wenchao Yu, Guangxiang Zeng, Ping Luo, Fuzhen Zhuang, Qing He, Zhongzhi Shi: Embedding with Autoencoder Regularization. ECML/PKDD (3) 2013: 208
1-223</td></tr><tr><td><a href="https://ping-luo.github.io/files/2013_App Recommendation A Contest between Satisfaction and Temptation.pdf">pdf</a></td><td>Peifeng Yin, Ping Luo, Wang-Chien Lee, Min Wang: App recommendation: a contest between satisfaction and temptation. WSDM 2013: 395-404</td></tr><tr><td><a href="https://ping-luo.github.io/files/2013_Triplex Transfer Learning Exploiting both Shared and Distinct Concepts for Text Classification.pdf">pdf</a></td><td>Fuzhen Zhuang, Ping Luo, Changying Du, Qing He, Zhongzhi Shi: Triplex transfer learning: exploiting both shared and distinct concepts for text classification. WSDM 2013: 425-434</td></tr></tbody></table><table><thead><tr><th>2012</th><th> </th></tr></thead><tbody><tr><td><a href="https://ping-luo.github.io/files/2012_Mining Distinction and Commonality across Multiple Domains Using Generative Model for Text Classification.pdf">pdf</a></td><td>Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, Zhongzhi Shi, Hui Xiong: Mining Distinction and Commonality across Multiple Domains Using Generative Model for Text Classification. IEEE Trans. Knowl. Data Eng. 24(11): 2025-2039 (2012)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2012_Incorporating Occupancy into Frequent Pattern Mining for High Quality Pattern Recommendation.pdf">pdf</a></td><td>Linpeng Tang, Lei Zhang, Ping Luo, Min Wang: Incorporating occupancy into frequent pattern mining for high quality pattern recommendation. CIKM 2012: 75-84</td></tr><tr><td><a href="https://ping-luo.github.io/files/2012_A Graph-Based Approach for Ontology Population with Named Entities.pdf">pdf</a></td><td>Wei Shen, Jianyong Wang, Ping Luo, Min Wang: A graph-based approach for ontology population with named entities. CIKM 2012: 345-354</td></tr><tr><td><a href="https://ping-luo.github.io/files/2012_Harnessing the Wisdom of the Crowds for Accurate Web Page Clipping.pdf">pdf</a></td><td>Lei Zhang, Linpeng Tang, Ping Luo, Enhong Chen, Limei Jiao, Min Wang, Guiquan Liu: Harnessing the wisdom of the crowds for accurate web page clipping. KDD 2012: 570-578</td></tr><tr><td><a href="https://ping-luo.github.io/files/2012_LIEGE Link Entities in Web Lists with Knowledge Base.pdf">pdf</a></td><td>Wei Shen, Jianyong Wang, Ping Luo, Min Wang: LIEGE::link entities in web lists with knowledge base. KDD 2012: 1424-1432</td></tr><tr><td><a href="https://ping-luo.github.io/files/2012_Towards Alias Detection Without String Similarity an Active Learning based Approach.pdf">pdf</a></td><td>Lili Jiang, Jianyong Wang, Ping Luo, Ning An, Min Wang: Towards alias detection without string similarity: an active learning based approach. SIGIR 2012: 1155-1156</td></tr><tr><td><a href="https://ping-luo.github.io/files/2012_A Straw Shows Which Way the Wind Blows Ranking Potentially Popular Items from Early Votes.pdf">pdf</a></td><td>Peifeng Yin, Ping Luo, Min Wang, Wang-Chien Lee: A straw shows which way the wind blows: ranking potentially popular items from early votes. WSDM 2012: 623-632</td></tr><tr><td><a href="https://ping-luo.github.io/files/2012_LINDEN linking named entities with knowledge base via semantic knowledge.pdf">pdf</a></td><td>Wei Shen, Jianyong Wang, Ping Luo, Min Wang: LINDEN: linking named entities with knowledge base via semantic knowledge. WWW 2012: 449-458</td></tr><tr><td><a href="https://ping-luo.github.io/files/2012_APOLLO a general framework for populating ontology with named entities via random walks on graphs.pdf">pdf</a></td><td>Wei Shen, Jianyong Wang, Ping Luo, Min Wang: APOLLO: a general framework for populating ontology with named entities via random walks on graphs. WWW (Companion Volume) 2012: 595-596</td></tr></tbody></table><table><thead><tr><th>2011</th><th> </th></tr></thead><tbody><tr><td><a href="https://ping-luo.github.io/files/2011_Exploiting associations between word clusters and document classes for cross-domain text categorization.pdf">pdf</a></td><td>Fuzhen Zhuang, Ping Luo, Hui Xiong, Qing He, Yuhong Xiong, Zhongzhi Shi: Exploiting associations between word clusters and document classes for cross-domain text categorization. Statistical Analysis and Data Mining 4(1): 100-114 (2011)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2011_AWETO Efficient Incremental Update and Querying in RDF Storage System.pdf">pdf</a></td><td>
1Xu Pu, Jianyong Wang, Ping Luo, Min Wang: AWETO: efficient incremental update and querying in rdf storage system. CIKM 2011: 2445-2448</td></tr><tr><td><a href="https://ping-luo.github.io/files/2011_Combining Supervised and Unsupervised Models via Unconstrained Probabilistic Embedding.pdf">pdf</a></td><td>Xudong Ma, Ping Luo, Fuzhen Zhuang, Qing He, Zhongzhi Shi, Zhiyong Shen: Combining Supervised and Unsupervised Models via Unconstrained Probabilistic Embedding. IJCAI 2011: 1396-1401</td></tr><tr><td><a href="https://ping-luo.github.io/files/2011_Prominent Streak Discovery in Sequence Data.pdf">pdf</a></td><td>Xiao Jiang, Chengkai Li, Ping Luo, Min Wang, Yong Yu: Prominent streak discovery in sequence data. KDD 2011: 1280-1288</td></tr><tr><td><a href="https://ping-luo.github.io/files/2011_REACTOR a framework for semantic relation extraction and tagging over enterprise data.pdf">pdf</a></td><td>Wei Shen, Jianyong Wang, Ping Luo, Min Wang, Conglei Yao: REACTOR: a framework for semantic relation extraction and tagging over enterprise data. WWW (Companion Volume) 2011: 121-122</td></tr></tbody></table><table><thead><tr><th>2010</th><th> </th></tr></thead><tbody><tr><td><a href="https://ping-luo.github.io/files/2010_Cross-Domain Learning from Multiple Sources A Consensus Regularization Perspective.pdf">pdf</a></td><td>Fuzhen Zhuang, Ping Luo, Hui Xiong, Yuhong Xiong, Qing He, Zhongzhi Shi: Cross-Domain Learning from Multiple Sources: A Consensus Regularization Perspective. IEEE Trans. Knowl. Data Eng. 22(12): 1664-1678 (2010)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2010_Collaborative Dual-PLSA Mining Distinction and Commonality across MultipleDomains for Classification.pdf">pdf</a></td><td>Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, Zhongzhi Shi, Hui Xiong: Collaborative Dual-PLSA: mining distinction and commonality across multiple domains for text classification. CIKM 2010: 359-368</td></tr><tr><td><a href="https://ping-luo.github.io/files/2010_D-LDA A Topic Modeling Approach without Constraint Generation for Semi-defined Classification.pdf">pdf</a></td><td>Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, Zhongzhi Shi: D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-defined Classification. ICDM 2010: 709-718</td></tr><tr><td><a href="https://ping-luo.github.io/files/2010_Topic Modeling Ensembles.pdf">pdf</a></td><td>Zhiyong Shen, Ping Luo, Shengwen Yang, Xukun Shen: Topic Modeling Ensembles. ICDM 2010: 1031-1036</td></tr><tr><td><a href="https://ping-luo.github.io/files/2010_Exploiting Associations between Word Clusters and Document Classes for Cross-domain Text Categorization.pdf">pdf</a></td><td>Fuzhen Zhuang, Ping Luo, Hui Xiong, Qing He, Yuhong Xiong, Zhongzhi Shi: Exploiting Associations between Word Clusters and Document Classes for Cross-Domain Text Categorization. SDM 2010: 13-24</td></tr></tbody></table><table><thead><tr><th>2009</th><th> </th></tr></thead><tbody><tr><td><a href="https://ping-luo.github.io/files/2009_Information-Theoretic Distance Measures for Clustering Validation Generalization and Normalization.pdf">pdf</a></td><td>Ping Luo, Hui Xiong, Guoxing Zhan, Junjie Wu, Zhongzhi Shi: Information-Theoretic Distance Measures for Clustering Validation: Generalization and Normalization. IEEE Trans. Knowl. Data Eng. 21(9): 1249-1262 (2009)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2009_OfCourse Web Content Discovery, Classification and Information Extraction for Online Course Materials.pdf">pdf</a></td><td>Yuhong Xiong, Ping Luo, Yong Zhao, Fen Lin, Shicong Feng, Baoyao Zhou, Liwei Zheng: OfCourse: web content discovery, classification and information extraction for online course materials. CIKM 2009: 2077-2078</td></tr><tr><td><a href="https://ping-luo.github.io/files/2009_Web article extraction for web printing a DOM+ visual based approach.pdf">pdf</a></td><td>Ping Luo, Jian Fan, Sam Liu, Fen Lin, Yuhong Xiong, Jerry Liu: Web article extraction for web printing: a DOM+visual based approach. ACM Symposium on Document Engineering 2009: 66-69</td></tr><tr><td><a href="https://ping-luo.github.io/files/2009_Topic Modeling for Sequences of Temporal Activities.pdf">pdf</a></td><td>Zhiyong Shen, Ping Luo, Yuhong Xiong, Jun Sun, Yi-Dong Shen: Topic Modeling for Sequences of Temporal Activities. ICDM 2009: 980-985</td></tr><tr><td><a href="https://ping-luo.github.io/files/2009_Towards Combining Web Classification and Web Information Extraction a Case Study.pdf">pdf</a></td><td>
1Ping Luo, Fen Lin, Yuhong Xiong, Yong Zhao, Zhongzhi Shi: Towards combining web classification and web information extraction: a case study. KDD 2009: 1235-1244</td></tr></tbody></table><table><thead><tr><th>2008</th><th> </th></tr></thead><tbody><tr><td><a href="https://ping-luo.github.io/files/2008_Transfer Learning from Multiple Source Domains via Consensus Regularization.pdf">pdf</a></td><td>Ping Luo, Fuzhen Zhuang, Hui Xiong, Yuhong Xiong, Qing He: Transfer learning from multiple source domains via consensus regularization. CIKM 2008: 103-112</td></tr></tbody></table><table><thead><tr><th>2007</th><th> </th></tr></thead><tbody><tr><td><a href="https://ping-luo.github.io/files/2007_Distributed data mining in grid computing environments.pdf">pdf</a></td><td>Ping Luo, Kevin Lü, Zhongzhi Shi, Qing He: Distributed data mining in grid computing environments. Future Generation Comp. Syst. 23(1): 84-91 (2007)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2007_A revisit of fast greedy heuristics for mapping a class of independent tasks onto heterogeneous computing systems.pdf">pdf</a></td><td>Ping Luo, Kevin Lü, Zhongzhi Shi: A revisit of fast greedy heuristics for mapping a class of independent tasks onto heterogeneous computing systems. J. Parallel Distrib. Comput. 67(6): 695-714 (2007)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2007_On Defining Partition Entropy by Inequalities.pdf">pdf</a></td><td>Ping Luo, Guoxing Zhan, Qing He, Zhongzhi Shi, Kevin Lü: On Defining Partition Entropy by Inequalities. IEEE Trans. Information Theory 53(9): 3233-3239 (2007)</td></tr><tr><td><a href="https://ping-luo.github.io/files/2007_Combination Methodologies of Multi-agent Hyper Surface Classifiers Design and Implementation Issues.pdf">pdf</a></td><td>Qing He, Xiu-Rong Zhao, Ping Luo, Zhongzhi Shi: Combination Methodologies of Multi-agent Hyper Surface Classifiers: Design and Implementation Issues. AIS-ADM 2007: 100-113</td></tr><tr><td><a href="https://ping-luo.github.io/files/2007_Distributed Classification in Peer-to-Peer Networks.pdf">pdf</a></td><td>Ping Luo, Hui Xiong, Kevin Lü, Zhongzhi Shi: Distributed classification in peer-to-peer networks. KDD 2007: 968-976</td></tr></tbody></table></div></div><div class="page__footer"><footer> <!-- start custom footer snippets --> <a href="/sitemap/">Sitemap</a> <!-- end custom footer snippets --><div class="page__footer-follow"><ul class="social-icons"><li><strong>Follow:</strong></li><li><a href="https://ping-luo.github.io/feed.xml"><i class="fa fa-fw fa-rss-square" aria-hidden="true"></i> Feed</a></li></ul></div><div class="page__footer-copyright">&copy; 2025 Ping Luo. Deployed by <a href="https://www.okcd00.tech" rel="nofollow">Dian Chen</a>. Powered by <a href="http://jekyllrb.com" rel="nofollow">Jekyll</a> &amp; <a href="https://github.com/academicpages/academicpages.github.io">AcademicPages</a>, a fork of <a href="https://mademistakes.com/work/minimal-mistakes-jekyll-theme/" rel="nofollow">Minimal Mistakes</a>.</div></footer></div>
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