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UID:192@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20240318T140000
DTEND;TZID=Europe/Paris:20240318T140000
DTSTAMP:20260825T131138Z
URL:https://isdm.umontpellier.fr/events/federated-conformal-prediction-mar
 ginal-and-training-conditional-validity/
SUMMARY:Federated Conformal Prediction: Marginal and Training-Conditional V
 alidity
DESCRIPTION:Room 01.124\, Building 5\, St Priest campus\n\nMachine Learning
  in Montpellier\, Theory &amp\; Practice\n\nIn this talk\, I present the o
 bjective of conformal prediction and overview some properties of the well-
 studied split estimator. Then\, I introduce a quantile-of-quantiles estima
 tor that allows constructing prediction sets in a one-shot federated learn
 ing setting. We investigate the properties of this estimator and how it ca
 n be used to build confidence sets with probabilistic coverage being margi
 nally or training-conditionally valid. Over a set of experiments\, we empi
 rically verify the quality of our results and show that it is possible to 
 output prediction sets with desired coverage\, in only one round of commun
 ication\, while recovering performances close to the centralized split met
 hod.\n\nMachine Learning in Montpellier\, Theory &amp\; Practice
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2026/06/ml-mtp-gC78d5.png
CATEGORIES:ML MTP
LOCATION:Campus Saint Priest - Batiment 5 - Salle 01.124\, 860 rue St Pries
 t\, Montpellier\, 
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=860 rue St Priest\, Montpel
 lier\, ;X-APPLE-RADIUS=100;X-TITLE=Campus Saint Priest - Batiment 5 - Sall
 e 01.124:geo:0,0
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TZID:Europe/Paris
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DTSTART:20231029T020000
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