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183    <h2 class="post-title">talks</h2>
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186      <p class="post-description">conference talks and poster presentations</p>
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201    <tr>
202      <th scope="col">Date</th>
203      <th scope="col">Venue</th>
204      <th scope="col">Title</th>
205      <th scope="col">Links</th>
206    </tr>
207  </thead>
208  <tbody>
209    <tr>
210      <th scope="row">2026-07-09</th>
211      <td><text class="venue">ICML 2026</text><br />
212          Seoul, South Korea </td>
213      <td>Functional decomposition and Shapley interactions for interpreting survival models</td>
214      <td>
215        <a href="https://github.com/bips-hb/survshapiq/blob/icml2026/assets/poster.pdf" target="_blank">[poster]</a>
216        <a href="https://openreview.net/forum?id=SldP4LGjdz" target="_blank">[paper]</a>
217      </td>
218    </tr>
219    <tr>
220      <th scope="row">2025-12-05</th>
221      <td><text class="venue">NeurIPS 2025</text><br />
222          San Diego, USA </td>
223      <td>Explaining similarity in vision-language encoders with weighted Banzhaf interactions</td>
224      <td>
225        <a href="https://github.com/hbaniecki/fixlip/blob/main/assets/poster.pdf" target="_blank">[poster]</a>
226        <a href="https://openreview.net/forum?id=on22Rx5A4F" target="_blank">[paper]</a>
227      </td>
228    </tr>
229    <tr>
230      <th scope="row">2025-04-25</th>
231      <td><text class="venue">ICLR 2025</text><br />
232          Singapore </td>
233      <td>Efficient and accurate explanation estimation with distribution compression</td>
234      <td>
235        <a href="https://github.com/hbaniecki/talks/blob/main/2025/iclr_efficient.pdf" target="_blank">[poster]</a>
236        <a href="https://openreview.net/forum?id=LiUfN9h0Lx" target="_blank">[paper]</a>
237      </td>
238    </tr>
239    <tr>
240      <th scope="row">2024-12-12</th>
241      <td><text class="venue">NeurIPS 2024</text><br />
242          Vancouver, Canada </td>
243      <td>shapiq: Shapley interactions for machine learning</td>
244      <td>
245        <a href="https://github.com/mmschlk/shapiq/blob/main/docs/source/_static/poster.pdf" target="_blank">[poster]</a>
246        <a href="https://openreview.net/forum?id=knxGmi6SJi" target="_blank">[paper]</a>
247      </td>
248    </tr>
249    <tr>
250      <th scope="row">2024-09-11</th>
251      <td><text class="venue">ECML PKDD 2024</text><br />
252          Vilnius, Lithuania </td>
253      <td>On the robustness of global feature effect explanations</td>
254      <td>
255        <a href="https://github.com/hbaniecki/talks/blob/main/2024/ecml_robustness_slides.pdf" target="_blank">[slides]</a>
256        <a href="https://arxiv.org/abs/2406.09069" target="_blank">[paper]</a>
257        <a href="https://github.com/hbaniecki/talks/blob/main/2024/ecml_robustness_poster.pdf" target="_blank">[poster]</a>
258      </td>
259    </tr>
260    <tr>
261      <th scope="row">2024-07-27</th>
262      <td><text class="venue">ICML 2024</text><br />
263          Vienna, Austria </td>
264      <td>Efficient and accurate explanation estimation with distribution compression</td>
265      <td>
266        <a href="https://github.com/hbaniecki/talks/blob/main/2024/icml_efficient.pdf" target="_blank">[poster]</a>
267        <a href="https://arxiv.org/abs/2406.18334" target="_blank">[paper]</a>
268      </td>
269    </tr>
270    <tr>
271      <th scope="row">2024-02-12</th>
272      <td><text class="venue">BIRS 2024</text><br />
273          Banff, Canada </td>
274      <td>Interpretable machine learning for time-to-event prediction in medicine and healthcare</td>
275      <td>
276        <a href="https://hbaniecki.com/birs2024" target="_blank">[slides]</a>
277        <a href="http://www.birs.ca/events/2024/5-day-workshops/24w5284/videos/watch/202402121501-Baniecki.html" target="_blank">[video]</a>
278      </td>
279    </tr>
280    <tr>
281      <th scope="row">2023-08-31</th>
282      <td><text class="venue">IJCAI 2023</text><br />
283          Macao, SAR China </td>
284      <td>Adversarial attacks and defenses in explainable artificial intelligence: A survey</td>
285      <td>
286        <a href="https://hbaniecki.com/ijcai2023" target="_blank">[slides]</a>
287        <a href="https://arxiv.org/abs/2306.06123" target="_blank">[paper]</a>
288      </td>
289    </tr>
290    <tr>
291      <th scope="row">2023-06-13</th>
292      <td><text class="venue">AIME 2023</text><br />
293          Portoroz, Slovenia </td>
294      <td>Hospital length of stay prediction based on multi-modal data towards trustworthy human-AI collaboration in radiomics</td>
295      <td>
296        <a href="https://hbaniecki.com/aime2023" target="_blank">[slides]</a>
297        <a href="https://doi.org/10.1007/978-3-031-34344-5_9" target="_blank">[paper]</a>
298      </td>
299    </tr>
300    <tr>
301      <th scope="row">2022-11-05</th>
302      <td><text class="venue">ML in PL 2022</text><br />
303          Warsaw, Poland </td>
304      <td>Interactive sequential analysis of a model improves the performance of human decision-making</td>
305      <td>
306        <a href="https://github.com/hbaniecki/talks/blob/main/2022/mlinpl_iema.pdf" target="_blank">[slides]</a>
307        <a href="https://doi.org/10.1007/s10618-023-00924-w" target="_blank">[paper]</a>
308        <a href="https://youtu.be/N1fLIeMpnKk" target="_blank">[video]</a>
309      </td>
310    </tr>
311    <tr>
312      <th scope="row">2022-09-21</th>
313      <td><text class="venue">ECML PKDD 2022</text><br />
314          Grenoble, France </td>
315      <td>Fooling partial dependence via data poisoning</td>
316      <td>
317        <a href="https://github.com/hbaniecki/talks/blob/main/2022/ecmlpkdd_fooling_slides.pdf" target="_blank">[slides]</a>
318        <a href="https://doi.org/10.1007/978-3-031-26409-2_8" target="_blank">[paper]</a>
319        <a href="https://github.com/hbaniecki/talks/blob/main/2022/ecmlpkdd_fooling_poster.pdf" target="_blank">[poster]</a>
320      </td>
321    </tr>
322    <tr>
323      <th scope="row">2022-08-08</th>
324      <td><text class="venue">JSM 2022</text><br />
325          Washington DC, USA </td>
326      <td>
326dalex: Responsible machine learning with interactive explainability and fairness in Python</td>
327      <td>
328        <a href="https://hbaniecki.com/jsm2022" target="_blank">[award]</a> 
329        <a href="https://github.com/hbaniecki/talks/blob/main/2022/jsm_dalex.pdf" target="_blank">[slides]</a>
330        <a href="https://ww2.amstat.org/meetings/jsm/2022/onlineprogram/AbstractDetails.cfm?abstractid=322304" target="_blank">[abstract]</a>
331        <a href="https://jmlr.org/papers/v22/20-1473.html" target="_blank">[paper]</a>
332      </td>
333    </tr>
334    <tr>
335      <th scope="row">2022-02-25</th>
336      <td><text class="venue">AAAI 2022</text><br />
337          virtual </td>
338      <td>Manipulating SHAP via adversarial data perturbations</td>
339      <td>
340        <a href="https://github.com/hbaniecki/talks/blob/main/2022/aaai_manipulating.pdf" target="_blank">[poster]</a>
341        <a href="https://doi.org/10.1609/aaai.v36i11.21590" target="_blank">[paper]</a>
342        <a href="https://github.com/hbaniecki/manipulating-shap" target="_blank">[demo]</a>
343      </td>
344    </tr>
345    <!-- <tr>
346      <th scope="row">2022-01-19</th>
347      <td><i>Ryanair Labs Madrid</i><br>
348          virtual </td>
349      <td>Explaining machine learning predictions with DALEX and beyond</td>
350      <td>
351        <a href="https://github.com/hbaniecki/talks/blob/main/2022/ryanair_explaining.pdf">[slides]</a>
352      </td>
353    </tr> -->
354    <tr>
355      <th scope="row">2021-11-06</th>
356      <td><text class="venue">ML in PL 2021</text><br />
357          virtual </td>
358      <td>Manipulating explainability and fairness in machine learning</td>
359      <td>
360        <a href="https://github.com/hbaniecki/talks/blob/main/2021/mlinpl_manipulating.pdf" target="_blank">[slides]</a>
361        <!-- [video] -->
362      </td>
363    </tr>
364    <tr>
365      <th scope="row">2021-07-07</th>
366      <td><text class="venue">useR! 2021</text><br />
367          virtual </td>
368      <td>Introduction to responsible machine learning</td>
369      <td>
370        <a href="https://github.com/MI2DataLab/ResponsibleML-UseR2021" target="_blank">[materials]</a>
371        <a href="https://www.youtube.com/watch?v=VaWmTDF3nQc" target="_blank">[video]</a>
372      </td>
373    </tr>
374    <tr>
375      <th scope="row">2021-07-05</th>
376      <td><text class="venue">useR! 2021</text><br />
377          virtual </td>
378      <td>Open the machine learning black-box with modelStudio &amp; Arena</td>
379      <td>
380        <a href="https://github.com/hbaniecki/talks/blob/main/2021/user_modelstudio.pdf" target="_blank">[slides]</a>
381        <a href="https://www.youtube.com/watch?v=tiR9ClOEaqM" target="_blank">[video]</a>
382        <a href="https://github.com/hbaniecki/user-21" target="_blank">[demo]</a>
383      </td>
384    </tr>
385    <tr>
386      <th scope="row">2021-02-06</th>
387      <td><text class="venue">AAAI 2021</text><br />
388          virtual </td>
389      <td>Responsible prediction making of COVID-19 mortality</td>
390      <td>
391        <a href="https://github.com/hbaniecki/talks/blob/main/2021/aaai_responsible.pdf" target="_blank">[poster]</a>
392        <a href="https://doi.org/10.1609/aaai.v35i18.17874" target="_blank">[paper]</a>
393        <a href="https://rai-covid.drwhy.ai" target="_blank">[demo]</a>
394      </td>
395    </tr>
396    <!-- <tr>
397      <th scope="row">2020-11-11</th>
398      <td><i>AI.SCIENCE Webinar</i><br>
399          virtual </td>
400      <td>DrWhy.AI - Tools for Explainable Artificial Intelligence</td>
401      <td>
402        <a href="https://github.com/hbaniecki/talks/blob/main/2020/aisc_tools4xai.pdf">[slides]</a>
403        <a href="https://www.youtube.com/watch?v=NxDrDNDmRKs">[video]</a>
404      </td>
405    </tr>
406    <tr>
407      <th scope="row">2020-11-04</th>
408      <td><i>X-Europe Webinar</i><br>
409          virtual </td>
410      <td>Tools for Explainable Artificial Intelligence</td>
411      <td>
412        <a href="https://github.com/hbaniecki/talks/blob/main/2020/xeurope_tools4xai.pdf">[slides]</a>
413        <a href="https://www.youtube.com/watch?v=EcDfSjR2lIw">[video]</a>
414      </td>
415    </tr>
416    <tr>
417      <th scope="row">2020-10-16</th>
418      <td><text class="venue">DS Summit 2020</text><br>
419          virtual </td>
420      <td><text class="title">XAI to support prediction making in COVID-19 pandemic</text></td>
421      <td>
422        <a href="https://github.com/hbaniecki/talks/blob/main/2020/dss_xaicovid.pdf">[slides]</a>
423      </td>
424    </tr> -->
425    <!-- <tr>
426      <th scope="row">2020-09-27</th>
427      <td><text class="venue">Why R? 2020</text><br>
428          virtual </td>
429      <td><text class="title">What's new in DrWhy.AI? (2020)</text></td>
430      <td>
431        <a href="https://github.com/hbaniecki/talks/blob/main/2020/whyr_drwhy.pdf">[slides]</a>
432        <a href="https://youtu.be/C7ac4A1t7sc?t=4685">[video]</a>
433      </td>
434    </tr> -->
435    <!-- <tr>
436      <th scope="row">2019-11-25</th>
437      <td><text class="venue">ML in PL 2019</text><br>
438          Warsaw, Poland</td>
439      <td>modelStudio: Interactive studio with explanations for ML predictive models</td>
440      <td>
441        <a href="https://github.com/hbaniecki/talks/blob/main/2019/mlinpl_modelstudio.pdf" target="_blank">[poster]</a>
442      </td>
443    </tr> -->
444    <!-- <tr>
445      <th scope="row">2019-09-29</th>
446      <td><text class="venue">Why R? 2019</text><br>
447          Warsaw, Poland</td>
448      <td><text class="title">DALEX + D3 = ?</text></td>
449      <td>
450        <a href="https://github.com/hbaniecki/talks/blob/main/2019/whyr_d3dalex.pdf">[slides]</a>
451      </td>
452    </tr> -->
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507<script defer src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
507 -->
508<script src="https://cdn.jsdelivr.net/npm/[email protected]/polyfill.min.js" integrity="sha256-UHghvpiWJZumUbnyJ9MJk6hs9O0U1PgI0GhZ4U1v6Is=" crossorigin="anonymous"></script>
508
509
510
511
512  </body>
513</html>

Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.