BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Economics | IP Paris - ECPv5.1.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Economics | IP Paris
X-ORIGINAL-URL:https://econ.ip-paris.fr
X-WR-CALDESC:Events for Department of Economics | IP Paris
BEGIN:VTIMEZONE
TZID:Europe/Helsinki
BEGIN:DAYLIGHT
TZOFFSETFROM:+0200
TZOFFSETTO:+0300
TZNAME:EEST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0300
TZOFFSETTO:+0200
TZNAME:EET
DTSTART:20251026T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20250912T110000
DTEND;TZID=Europe/Helsinki:20250912T120000
DTSTAMP:20260411T085634
CREATED:20250901T104406Z
LAST-MODIFIED:20250901T104406Z
UID:15862-1757674800-1757678400@econ.ip-paris.fr
SUMMARY:Futoshi Furami (Osaka University) "Uncertainty Quantification through Calibration in Classification Problems"
DESCRIPTION:Futoshi FURAMI – https://sites.google.com/view/futoshifutami/home (invité de Ikko Yamane) \nLe vendredi 12 septembre 2025\nde 11h à 12h (café d’accueil à partir de 10h40)\nà l’ENSAI\, Campus de Ker Lann à Bruz – salle 10 \nTitle :Uncertainty Quantification through Calibration in Classification Problems \nAbstract:In recent years\, in high-risk domains such as medical diagnosis\, weather forecasting\, and financial risk management\, there has been a strong demand for the reliability of probabilities output by machine learning models. A representative measure for assessing this reliability is calibration\, which evaluates the degree of agreement between predicted probabilities and the actual frequency of events. However\, it has been reported that modern deep learning models often exhibit poor calibration\, and improving this issue has become an urgent challenge.\nIn this talk\, I will introduce basic ideas related to calibration\, as well as our recent research\, including generalization error analysis of calibration errors based on statistical learning theory\, and correction algorithms utilizing PAC-Bayesian methods. \n
URL:https://econ.ip-paris.fr/event/futoshi-furami-osaka-university-uncertainty-quantification-through-calibration-in-classification-problems/
CATEGORIES:Seminars,Statistics
END:VEVENT
END:VCALENDAR