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:392@isdm.umontpellier.fr DTSTART;TZID=Europe/Paris:20250217T140000 DTEND;TZID=Europe/Paris:20250217T140000 DTSTAMP:20260828T114449Z URL:https://isdm.umontpellier.fr/events/rethinking-early-stopping-refine-t hen-calibrate-3/ SUMMARY:Rethinking Early Stopping: Refine\, Then Calibrate DESCRIPTION:Room 02.022\, Building 5\, St Priest campus\n\nMachine Learning in Montpellier\, Theory &\; Practice\n\nMachine learning classifiers o ften produce probabilistic predictions that are critical for accurate and interpretable decision-making in various domains. The quality of these pre dictions is generally evaluated with proper losses like cross-entropy\, wh ich decompose into two components: calibration error assesses general unde r/overconfidence\, while refinement error measures the ability to distingu ish different classes. In this paper\, we provide theoretical and empirica l evidence that these two errors are not minimized simultaneously during t raining. Selecting the best training epoch based on validation loss thus l eads to a compromise point that is suboptimal for both calibration error a nd\, mostimportantly\, refinement error. To address this\, we introduce a new metric for early stopping and hyperparameter tuning that makes it poss ible to minimize refinement error during training. The calibration error i s minimized after training\, using standard techniques. Our method integra tes seamlessly with any architecture and consistently improves performance across diverse classification tasks. [ https://isdm.umontpellier.fr/arxiv .org/abs/2501.19195 | https://isdm.umontpellier.fr/arxiv.org/abs/2501.1919 5 ]\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:Room 02.022\, Building 5\, St Priest Campus\, Montpellier\, 34000\ , France X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=St Priest Campus\, Montpell ier\, 34000\, France;X-APPLE-RADIUS=100;X-TITLE=Room 02.022\, Building 5:g eo:0,0 END:VEVENT BEGIN:VTIMEZONE TZID:Europe/Paris X-LIC-LOCATION:Europe/Paris BEGIN:STANDARD DTSTART:20241027T020000 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET END:STANDARD END:VTIMEZONE END:VCALENDAR