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TZID:Europe/Paris
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DTSTART:20260329T010000
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DTSTART:20261025T010000
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DTSTART;TZID=Europe/Paris:20260415T110000
DTEND;TZID=Europe/Paris:20260415T120000
DTSTAMP:20260415T015737
CREATED:20260410T085407Z
LAST-MODIFIED:20260410T085408Z
UID:21253-1776250800-1776254400@www.math.ens.psl.eu
SUMMARY:Conformal Prediction for Uncertainty Quantification in Machine Learning: Recent Advances
DESCRIPTION:Machine learning models are often seen as black-box systems that output point predictions without indicating how confident they are in those predictions. Recently\, Conformal Prediction (CP) has emerged as a powerful framework to address this issue by transforming point predictions into set-valued predictions with probabilistic guarantees. In this talk\, I will introduce CP and briefly present some key challenges and recent advances in the area. I will first discuss how to perform CP in a Federated Learning setting\, showing that a single round of communication is sufficient to match the performance of centralized approaches. I will then turn to the question of efficiency control in CP\, where finite-sample guarantees can be obtained by viewing CP as a minimum-volume set estimation problem. \n\n\n\nThis talk is based on the following papers:  \n\n\n\nP. Humbert\, B. Le Bars\, A. Bellet\, S. Arlot. One-Shot Federated Conformal Prediction\, ICML 2023 \n\n\n\nP. Humbert\, B. Le Bars\, A. Bellet\, S. Arlot. Marginal and Training-Conditional Guarantees in One-Shot Federated Conformal Prediction\, Arxiv preprint 2024 \n\n\n\nB. Le Bars\, P. Humbert. On Volume Minimization in Conformal Regression\, ICML 2025
URL:https://www.math.ens.psl.eu/evenement/conformal-prediction-for-uncertainty-quantification-in-machine-learning-recent-advances/
LOCATION:Salle W
CATEGORIES:CSD seminar
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