BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Institut de Science des Données - ECPv6.16.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Institut de Science des Données
X-ORIGINAL-URL:https://isdm.umontpellier.fr
X-WR-CALDESC:Évènements pour Institut de Science des Données
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Paris
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20240331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20241027T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20251026T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20250310T153000
DTEND;TZID=Europe/Paris:20250310T153000
DTSTAMP:20260530T015549
CREATED:20250603T090108Z
LAST-MODIFIED:20250603T090108Z
UID:9941-1741620600-1741620600@isdm.umontpellier.fr
SUMMARY:Distributional Matrix Completion via Nearest Neighbors in the Wasserstein Space
DESCRIPTION:Campus St Priest (860 Rue Saint Priest 34095 Montpellier Cedex 5)\, bat. 5\, Room: 02.124\nMachine Learning in Montpellier\, Theory & Practice\nJacob Feitelberg \nWe study the problem of distributional matrix completion: Given a sparsely observed matrix of empirical distributions\, we seek to impute the true distributions associated with both observed and unobserved matrix entries. This is a generalization of traditional matrix completion where the observations per matrix entry are scalar-valued. To do so\, we utilize tools from optimal transport to generalize the nearest neighbors method to the distributional setting. Under a suitable latent factor model on probability distributions\, we establish that our method recovers the distributions in the Wasserstein metric. We demonstrate through simulations that our method (i) provides better distributional estimates for an entry compared to using observed samples for that entry alone\, (ii) yields accurate estimates of distributional quantities such as standard deviation and value-at-risk\, and (iii) inherently supports heteroscedastic distributions. In addition\, we demonstrate our method on a real-world quarterly earnings predictions dataset. We also prove novel asymptotic results for Wasserstein barycenters over one-dimensional distributions. \n            Visio
URL:https://isdm.umontpellier.fr/event/distributional-matrix-completion-via-nearest-neighbors-in-the-wasserstein-space-2/
CATEGORIES:Séminaire
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/2025/02/ml-mpt-1.jpg
END:VEVENT
END:VCALENDAR