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DTSTART;TZID=America/New_York:20260514T160000
DTEND;TZID=America/New_York:20260514T170000
DTSTAMP:20260520T221350
CREATED:20260330T154547Z
LAST-MODIFIED:20260502T220412Z
UID:10003926-1778774400-1778778000@cmsa.fas.harvard.edu
SUMMARY:Polynomial invariants of conjugation over finite fields
DESCRIPTION:Algebra Seminar \nSpeaker: Aryaman Maithani\, University of Utah \nTitle: Polynomial invariants of conjugation over finite fields\n\nAbstract: Consider the conjugation action of GL₂(K) on the polynomial ring K[X₂ₓ₂].\nWhen K is an infinite field\, the ring of invariants is a polynomial ring generated by the trace and the determinant.\nWe describe the ring of invariants when K is a finite field\, and show that it is a hypersurface.\n  \n 
URL:https://cmsa.fas.harvard.edu/event/algebra-seminar_51426/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Algebra Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Algebra-Seminar-5.14.26.2.docx-scaled.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260518T150000
DTEND;TZID=America/New_York:20260518T160000
DTSTAMP:20260520T221350
CREATED:20260413T151244Z
LAST-MODIFIED:20260514T185707Z
UID:10003933-1779116400-1779120000@cmsa.fas.harvard.edu
SUMMARY:Abelian duality via derived geometry
DESCRIPTION:Quantum Field Theory and Physical Mathematics Seminar \nSpeaker: Owen Gwilliam\, UMass Amherst \nTitle: Abelian duality via derived geometry \nAbstract: We discuss how to synthesize differential cohomology and the BV formalism to describe generalized Maxwell theories (or abelian p-form gauge theories)\, and how this framework allows a succinct formulation of abelian duality. Given time\, we will discuss how these methods apply to the 6d self-dual 2-form gauge theory that appears as part of the 6d N=(1\,0) and (2\,0) superconformal theories known as abelian tensor multiplets. This is joint work in progress with Chris Elliott\, Ingmar Saberi\, and Brian Williams. \n 
URL:https://cmsa.fas.harvard.edu/event/qft_51826/
LOCATION:CMSA Room G02\, 20 Garden Street\, Cambridge\, MA\, 02138
CATEGORIES:Quantum Field Theory and Physical Mathematics
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-QFT-and-Physical-Mathematics-5.18.26.docx-scaled.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260519T161500
DTEND;TZID=America/New_York:20260519T180000
DTSTAMP:20260520T221350
CREATED:20260511T180006Z
LAST-MODIFIED:20260511T180006Z
UID:10003944-1779207300-1779213600@cmsa.fas.harvard.edu
SUMMARY:Geometry and Quantum Theory Seminar
DESCRIPTION:Joint Math/CMSA Geometry and Quantum Theory Seminar \nSpeaker: Vasily Krylov\, Harvard
URL:https://cmsa.fas.harvard.edu/event/quantumgeo_51926/
LOCATION:Science Center 507\, 1 Oxford Street\, Cambridge\, 02138
CATEGORIES:Geometry and Quantum Theory Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260520T140000
DTEND;TZID=America/New_York:20260520T150000
DTSTAMP:20260520T221350
CREATED:20260429T133019Z
LAST-MODIFIED:20260429T143145Z
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260923T120000
DTEND;TZID=America/New_York:20260923T130000
DTSTAMP:20260520T221350
CREATED:20260423T161606Z
LAST-MODIFIED:20260518T134620Z
UID:10003939-1790164800-1790168400@cmsa.fas.harvard.edu
SUMMARY:CMSA Q&A Seminar: Hugh Woodin\, Harvard
DESCRIPTION:CMSA Q&A Seminar \nSpeaker: Hugh Woodin\, Harvard \nTitle: Truth\, proof\, and AI \n  \n 
URL:https://cmsa.fas.harvard.edu/event/cmsaqa_92326/
LOCATION:Common Room\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:CMSA Q&A Seminar
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