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:159@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20250527T160000
DTEND;TZID=Europe/Paris:20250527T170000
DTSTAMP:20260602T130233Z
URL:https://isdm.umontpellier.fr/events/vertical-federated-learning-with-m
 issing-features-during-training-and-inference-2/
SUMMARY:Vertical Federated Learning with Missing Features During Training a
 nd Inference
DESCRIPTION:Machine Learning in Montpellier\, Theory &amp\; Practice - Pedr
 o Valdeira\n\nVertical federated learning trains models from feature-parti
 tioned datasets across multiple clients\, who collaborate without sharing 
 their local data. Standard approaches assume that all feature partitions a
 re available during both training and inference. Yet\, in practice\, this 
 assumption rarely holds\, as for many samples only a subset of the clients
  observe their partition. However\, not utilizing incomplete samples durin
 g training harms generalization\, and not supporting them during inference
  limits the utility of the model. [...]\n\nVertical Federated Learning\, M
 issing Feature Blocks\, LabéliséHallesIA
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2025/02/ml-mpt-1.jpg
END:VEVENT
BEGIN:VTIMEZONE
TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
BEGIN:DAYLIGHT
DTSTART:20250330T030000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
END:DAYLIGHT
END:VTIMEZONE
END:VCALENDAR