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home/about name --> 101 102 </a> 103 </li> 104 105 <!-- CV --> 106 <li class="nav-item"> 107 <a class="nav-link" href="/assets/pdf/cv_hbaniecki.pdf"> 108 cv 109 </a> 110 </li> 111 112 <!-- Other pages --> 113 114 115 116 117 118 119 120 121 122 <li class="nav-item "> 123 <a class="nav-link" href="/news/"> 124 news 125 126 </a> 127 </li> 128 129 130 131 <li class="nav-item "> 132 <a class="nav-link" href="/publications/"> 133 publications 134 135 </a> 136 </li> 137 138 139 140 <li class="nav-item "> 141 <a class="nav-link" href="/software/"> 142 software 143 144 </a> 145 </li> 146 147 148 149 <li class="nav-item active"> 150 <a class="nav-link" href="/talks/"> 151 talks 152 153 <span class="sr-only">(current)</span> 154 155 </a> 156 </li> 157 158 159 160 <li class="nav-item "> 161 <a class="nav-link" href="/teaching/"> 162 teaching 163 164 </a> 165 </li> 166 167 168 169 </ul> 170 </div> 171 </div> 172 </nav> 173 174</header> 175 176 177 <!-- Content --> 178 179 <div class="container mt-4"> 180 <div class="post"> 181 182 <header class="post-header"> 183 <h2 class="post-title">talks</h2> 184 185 186 <p class="post-description">conference talks and poster presentations</p> 187 188 </header> 189 190 <article> 191 <div class="teaching"> 192 193<table class="table table-hover table-sm table-fixborder"> 194 <colgroup> 195 <col style="width:12%" /> 196 <col style="width:19%" /> 197 <col style="width:53%" /> 198 <col style="width:16%" /> 199 </colgroup> 200 <thead> 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 & 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> --> 453 </tbody> 454</table> 455 456</div> 457 458 </article> 459 460</div> 461 </div> 462 463 <!-- Footer --> 464 465 466<footer class="fixed-bottom"> 467 <div class="container mt-0"> 468 ©
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