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DTSTART;TZID=America/New_York:20260520T140000
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DTSTAMP:20260507T201018
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UID:10003942-1779285600-1779289200@cmsa.fas.harvard.edu
SUMMARY:Separation of timescales controls feature learning and overfitting in large neural networks
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Pierfrancesco Urbani\, Universite Paris-Saclay\, CNRS\, CEA\, Institut de physique theorique \nTitle: Separation of timescales controls feature learning and overfitting in large neural networks \nAbstract: To understand the inductive bias and generalization capabilities of large\, overparameterized machine learning models\, it is essential to analyze the dynamics of their training algorithms. Using dynamical mean field theory we investigate the learning dynamics of large two-layer neural networks. Our findings reveal that\, for networks with a large width\, the training process exhibits a separation of timescales phenomenon. This leads to several key observations:\n1. The emergence of a slow timescale linked to the growth in Gaussian/Rademacher complexity of the network;\n2. An inductive bias favoring low complexity when the initial model complexity is sufficiently small;\n3. A dynamical decoupling between feature learning and overfitting phases;\n4. A non-monotonic trend in test error\, characterized by a “feature unlearning” regime at later stages of training.\nJoint work with Andrea Montanari. \n  \n 
URL:https://cmsa.fas.harvard.edu/event/newtech_52026/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-5.20.2026.docx.png
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260903T090000
DTEND;TZID=America/New_York:20260904T170000
DTSTAMP:20260507T201018
CREATED:20260217T174509Z
LAST-MODIFIED:20260217T174509Z
UID:10003846-1788426000-1788541200@cmsa.fas.harvard.edu
SUMMARY:Big Data Conference 2026
DESCRIPTION:Big Data Conference 2026 \nDates: Sep. 3–4\, 2026 \nLocation: Harvard University CMSA\, 20 Garden Street\, Cambridge & via Zoom \nThe Big Data Conference features speakers from the Harvard community as well as scholars from across the globe\, with talks focusing on computer science\, statistics\, math and physics\, and economics. \nDetails TBA \n 
URL:https://cmsa.fas.harvard.edu/event/bigdata_2026/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Big Data Conference,Conference,Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260908T090000
DTEND;TZID=America/New_York:20260911T170000
DTSTAMP:20260507T201018
CREATED:20260217T174544Z
LAST-MODIFIED:20260217T174544Z
UID:10003847-1788858000-1789146000@cmsa.fas.harvard.edu
SUMMARY:The Geometry of Machine Learning 2026
DESCRIPTION:The Geometry of Machine Learning 2026 \nDates: September 8–11\, 2026 \nLocation: Harvard CMSA\, Room G10\, 20 Garden Street\, Cambridge MA 02138 \nOrganizers: Michael R. Douglas (CMSA) and Mike Freedman (CMSA) \n  \nDetails TBA \n  \nSupport provided by Logical Intelligence. \n \n  \n 
URL:https://cmsa.fas.harvard.edu/event/gml_2026/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Conference,Event
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