BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.4.0.1//EN TZID:Europe/Paris X-WR-TIMEZONE:Europe/Paris BEGIN:VEVENT UID:379@isdm.umontpellier.fr DTSTART;TZID=Europe/Paris:20240429T140000 DTEND;TZID=Europe/Paris:20240429T140000 DTSTAMP:20260828T112221Z URL:https://isdm.umontpellier.fr/events/interpretable-machine-learning-mod els-for-predicting-with-missing-values-2/ SUMMARY:Interpretable machine learning models for predicting with missing v alues DESCRIPTION:Room 01.124\, Building 5\, St Priest campus\n\nMachine Learning in Montpellier\, Theory &\; Practice\n\nMachine learning models are fr equently used when inputs are missing during training or prediction\, pote ntially leading to increased bias or impractical models without imputing u nobserved variables. Imputing missing values is often inadequate and hard to interpret\, especially with complex functions. This talk addresses the challenge of predicting with missing data at test time\, highlighting the need for interpretable and practical models\, crucial in critical sectors like healthcare. In this talk\, I present two novel approaches: the Shared Pattern Sparsity Model (SPSM) for scenarios with recurrent missing data p atterns\, which promotes efficient data use and interpretability without r eliance on imputation\; and MINTY\, a sparse linear rule model\, regulariz ed to minimize dependence on features with missing values. This model allo ws a trade-off between goodness of fit\, interpretability\, and robustness to missing values at test time. Additionally\, I'\;ll share early res ults from a project developing a predictive model for sequential risk scor es sensitive to missing values across time steps. In collaboration with th e Traumabase network\, we'\;ll conduct a user study with clinical prof essionals to assess the effectiveness of interpretable models in handling missing data.\n\nMachine Learning in Montpellier\, Theory &\; Practice ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/ 2026/06/ml-mtp-gC78d5.png CATEGORIES:ML MTP LOCATION:Saint Priest Campus - Building 5 - Room 02.124\, 860 rue St Priest \, Montpellier\, X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=860 rue St Priest\, Montpel lier\, ;X-APPLE-RADIUS=100;X-TITLE=Saint Priest Campus - Building 5 - Room 02.124:geo:0,0 END:VEVENT BEGIN:VTIMEZONE TZID:Europe/Paris X-LIC-LOCATION:Europe/Paris BEGIN:DAYLIGHT DTSTART:20240331T030000 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST END:DAYLIGHT END:VTIMEZONE END:VCALENDAR