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BEGIN:VEVENT
UID:921@lincs.fr
DTSTART;TZID=Europe/Paris:20251001T140000
DTEND;TZID=Europe/Paris:20251001T150000
DTSTAMP:20251020T101650Z
URL:https://www.lincs.fr/events/statistical-learning-from-high-dimensional
 -and-distributed-data/
SUMMARY:Statistical learning from high-dimensional and distributed data
DESCRIPTION:Modern learning algorithms must tackle real-world problems
 involving complex\, heterogeneous\, often unlabeled\, high-dimensional\,
 and potentially large-scale and/or distributed data.\n\nIn this talk\, the
 speaker will present a statistical learning approach focused on the design
 of latent variable models\, with approximation capabilities and learning
 guarantees. He will begin by introducing mixture-of-experts models designed
 for heterogeneous data and high-dimensional functional predictors\, which
 may be noisy\, and their training via regularization methods\, enabling
 sparse and interpretable representations.\n\nFinally\, when data are
 inherently distributed and/or constrained by confidentiality requirements\,
 the speaker will present federated learning and aggregation strategy for
 (statistical or neural) models trained in parallel.\n\n&nbsp\;
CATEGORIES:Seminars,Youtube
LOCATION:Zoom + Amphi 4 chez Télécom-Paris\, 19 Place Marguerite Perey\,
 Palaiseau\,  91120\, France
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 Palaiseau\,  91120\, France;X-APPLE-RADIUS=100;X-TITLE=Zoom + Amphi 4 chez
 Télécom-Paris:geo:0,0
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TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
BEGIN:DAYLIGHT
DTSTART:20250330T030000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
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