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X-ORIGINAL-URL:https://cmsa.fas.harvard.edu
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BEGIN:VTIMEZONE
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TZOFFSETFROM:-0500
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DTSTART:20190310T070000
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TZOFFSETTO:-0500
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DTSTART:20191103T060000
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TZNAME:EDT
DTSTART:20200308T070000
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TZOFFSETTO:-0500
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DTSTART:20201101T060000
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TZOFFSETTO:-0400
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201005T103000
DTEND;TZID=America/New_York:20201005T120000
DTSTAMP:20260508T090250
CREATED:20240201T023640Z
LAST-MODIFIED:20240201T023640Z
UID:10001527-1601893800-1601899200@cmsa.fas.harvard.edu
SUMMARY:10/05/2020 Math Physics Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/10-05-2020-math-physics-seminar/
CATEGORIES:Mathematical Physics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201005T103000
DTEND;TZID=America/New_York:20201005T120000
DTSTAMP:20260508T090250
CREATED:20240209T104813Z
LAST-MODIFIED:20240209T104813Z
UID:10001831-1601893800-1601899200@cmsa.fas.harvard.edu
SUMMARY:4/15/2020 Quantum Matter seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/4-15-2020-quantum-matter-seminar/
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201006T113000
DTEND;TZID=America/New_York:20201006T123000
DTSTAMP:20260508T090250
CREATED:20240201T023837Z
LAST-MODIFIED:20240201T023837Z
UID:10001528-1601983800-1601987400@cmsa.fas.harvard.edu
SUMMARY:10/6/2020 Computer Science for Mathematicians
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/10-6-2020-computer-science-for-mathematicians/
CATEGORIES:Computer Science for Mathematicians Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201007T103000
DTEND;TZID=America/New_York:20201007T120000
DTSTAMP:20260508T090250
CREATED:20240201T022156Z
LAST-MODIFIED:20240201T022156Z
UID:10001525-1602066600-1602072000@cmsa.fas.harvard.edu
SUMMARY:10/7/2020 Quantum Matter Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/10-7-2020-quantum-matter-seminar/
CATEGORIES:Quantum Matter
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201008T103000
DTEND;TZID=America/New_York:20201008T120000
DTSTAMP:20260508T090250
CREATED:20240201T022038Z
LAST-MODIFIED:20240201T022038Z
UID:10001524-1602153000-1602158400@cmsa.fas.harvard.edu
SUMMARY:10/8/2020 Quantum Matter Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/10-8-2020-quantum-matter-seminar/
CATEGORIES:Quantum Matter
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201014T090000
DTEND;TZID=America/New_York:20201014T100000
DTSTAMP:20260508T090250
CREATED:20240127T031011Z
LAST-MODIFIED:20240507T194446Z
UID:10001499-1602666000-1602669600@cmsa.fas.harvard.edu
SUMMARY:Statistical\, mathematical\, and computational aspects of noisy intermediate-scale quantum computers 
DESCRIPTION:Speaker: Gil Kalai (Hebrew University and IDC Herzliya) \nTitle: Statistical\, mathematical\, and computational aspects of noisy intermediate-scale quantum computers \nAbstract: Noisy intermediate-scale quantum (NISQ) Computers hold the key for important theoretical and experimental questions regarding quantum computers. In the lecture I will describe some questions about mathematics\, statistics and computational complexity which arose in my study of NISQ systems and are related to \n\na) My general argument “against” quantum computers\,\nb) My analysis (with Yosi Rinott and Tomer Shoham) of the Google 2019 “quantum supremacy” experiment.\nRelevant papers:\nYosef Rinott\, Tomer Shoham and Gil Kalai\, Statistical aspects of the quantum supremacy demonstration\, https://gilkalai.files.wordpress.com/2019/11/stat-quantum2.pdf\nGil Kalai\, The Argument against Quantum Computers\, the Quantum Laws of Nature\, and Google’s Supremacy Claims\, https://gilkalai.files.wordpress.com/2020/08/laws-blog2.pdf\nGil Kalai\, Three puzzles on mathematics\, computations\, and games\, https://gilkalai.files.wordpress.com/2019/09/main-pr.pdf
URL:https://cmsa.fas.harvard.edu/event/10-14-2020-colloquium/
CATEGORIES:Colloquium
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Colloquium-10.14.20-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201014T103000
DTEND;TZID=America/New_York:20201014T120000
DTSTAMP:20260508T090250
CREATED:20240127T031114Z
LAST-MODIFIED:20240127T031114Z
UID:10001500-1602671400-1602676800@cmsa.fas.harvard.edu
SUMMARY:10/14/2020 Quantum Matter Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/10-14-2020-quantum-matter-seminar/
CATEGORIES:Quantum Matter
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201014T140000
DTEND;TZID=America/New_York:20201014T150000
DTSTAMP:20260508T090250
CREATED:20240127T031227Z
LAST-MODIFIED:20240127T031227Z
UID:10001501-1602684000-1602687600@cmsa.fas.harvard.edu
SUMMARY:10/14/2020 RM&PT Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/10-14-2020-rmpt-seminar/
CATEGORIES:Random Matrix & Probability Theory Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201014T150000
DTEND;TZID=America/New_York:20201014T160000
DTSTAMP:20260508T090250
CREATED:20240201T021720Z
LAST-MODIFIED:20240515T192014Z
UID:10001522-1602687600-1602691200@cmsa.fas.harvard.edu
SUMMARY:Triple Descent and a Fine-Grained Bias-Variance Decomposition
DESCRIPTION:Speaker: Jeffrey Pennington\, Google Brain \nTitle: Triple Descent and a Fine-Grained Bias-Variance Decomposition \nAbstract: Classical learning theory suggests that the optimal generalization performance of a machine learning model should occur at an intermediate model complexity\, striking a balance between simpler models that exhibit high bias and more complex models that exhibit high variance of the predictive function. However\, such a simple trade-off does not adequately describe the behavior of many modern deep learning models\, which simultaneously attain low bias and low variance in the heavily overparameterized regime. Recent efforts to explain this phenomenon theoretically have focused on simple settings\, such as linear regression or kernel regression with unstructured random features\, which are too coarse to reveal important nuances of actual neural networks. In this talk\, I will describe a precise high-dimensional asymptotic analysis of Neural Tangent Kernel regression that reveals some of these nuances\, including non-monotonic behavior deep in the overparameterized regime. I will also present a novel bias-variance decomposition that unambiguously attributes these surprising observations to particular sources of randomness in the training procedure.
URL:https://cmsa.fas.harvard.edu/event/10-14-2020-new-technologies-seminar/
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-New-Technologies-in-Mathematics-10.14.20.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201015T103000
DTEND;TZID=America/New_York:20201015T120000
DTSTAMP:20260508T090250
CREATED:20240201T021839Z
LAST-MODIFIED:20240201T021839Z
UID:10001523-1602757800-1602763200@cmsa.fas.harvard.edu
SUMMARY:10/15/2020 Quantum Matter Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/10-15-2020-quantum-matter-seminar/
LOCATION:Virtual
CATEGORIES:Quantum Matter
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201019T103000
DTEND;TZID=America/New_York:20201019T113000
DTSTAMP:20260508T090250
CREATED:20240127T030908Z
LAST-MODIFIED:20240127T030908Z
UID:10001498-1603103400-1603107000@cmsa.fas.harvard.edu
SUMMARY:10/19/2020 Math Physics Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/10-19-2020-math-physics-seminar/
CATEGORIES:Mathematical Physics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201020T113000
DTEND;TZID=America/New_York:20201020T123000
DTSTAMP:20260508T090250
CREATED:20240127T030632Z
LAST-MODIFIED:20240127T030632Z
UID:10001497-1603193400-1603197000@cmsa.fas.harvard.edu
SUMMARY:10/20/2020 Computer Science for Math
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/10-20-2020-computer-science-for-math/
CATEGORIES:Computer Science for Mathematicians Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201021T103000
DTEND;TZID=America/New_York:20201021T120000
DTSTAMP:20260508T090250
CREATED:20240127T023403Z
LAST-MODIFIED:20240127T023403Z
UID:10001495-1603276200-1603281600@cmsa.fas.harvard.edu
SUMMARY:10/21/2020 Quantum Matter Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/10-21-2020-quantum-matter-seminar/
CATEGORIES:Quantum Matter
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201022T103000
DTEND;TZID=America/New_York:20201022T120000
DTSTAMP:20260508T090250
CREATED:20240127T023302Z
LAST-MODIFIED:20240127T023302Z
UID:10001494-1603362600-1603368000@cmsa.fas.harvard.edu
SUMMARY:10/22/2020 Quantum Matter Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/10-22-2020-quantum-matter-seminar/
CATEGORIES:Quantum Matter
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201027T113000
DTEND;TZID=America/New_York:20201027T123000
DTSTAMP:20260508T090250
CREATED:20240127T022958Z
LAST-MODIFIED:20240127T022958Z
UID:10001491-1603798200-1603801800@cmsa.fas.harvard.edu
SUMMARY:10/27/2020 Computer Science for Mathematicians
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/10-27-2020-computer-science-for-mathematicians/
CATEGORIES:Computer Science for Mathematicians Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201028T103000
DTEND;TZID=America/New_York:20201028T120000
DTSTAMP:20260508T090250
CREATED:20240127T023513Z
LAST-MODIFIED:20240127T023513Z
UID:10001496-1603881000-1603886400@cmsa.fas.harvard.edu
SUMMARY:10/28/2020 Strongly Correlated Quantum Materials
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/10-28-2020-strongly-correlated-quantum-materials/
CATEGORIES:Strongly Correlated Quantum Materials and High-Temperature Superconductors
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201028T140000
DTEND;TZID=America/New_York:20201028T150000
DTSTAMP:20260508T090250
CREATED:20240127T023200Z
LAST-MODIFIED:20240127T023200Z
UID:10001493-1603893600-1603897200@cmsa.fas.harvard.edu
SUMMARY:10/28/2020 RM&PT seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/10-28-2020-rmpt-seminar/
CATEGORIES:Random Matrix & Probability Theory Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201028T150000
DTEND;TZID=America/New_York:20201028T160000
DTSTAMP:20260508T090250
CREATED:20240127T031359Z
LAST-MODIFIED:20240515T201157Z
UID:10001502-1603897200-1603900800@cmsa.fas.harvard.edu
SUMMARY:Generalization bounds for rational self-supervised learning algorithms\, or "Understanding generalizations requires rethinking deep learning"
DESCRIPTION:Speakers: Boaz Barak and Yamini Bansal\, Harvard University Dept. of Computer Science \nTitle: Generalization bounds for rational self-supervised learning algorithms\, or “Understanding generalizations requires rethinking deep learning” \nAbstract: The generalization gap of a learning algorithm is the expected difference between its performance on the training data and its performance on fresh unseen test samples. Modern deep learning algorithms typically have large generalization gaps\, as they use more parameters than the size of their training set. Moreover the best known rigorous bounds on their generalization gap are often vacuous. In this talk we will see a new upper bound on the generalization gap of classifiers that are obtained by first using self-supervision to learn a complex representation of the (label free) training data\, and then fitting a simple (e.g.\, linear) classifier to the labels. Such classifiers have become increasingly popular in recent years\, as they offer several practical advantages and have been shown to approach state-of-art results. We show that (under the assumptions described below) the generalization gap of such classifiers tends to zero as long as the complexity of the simple classifier is asymptotically smaller than the number of training samples. We stress that our bound is independent of the complexity of the representation that can use an arbitrarily large number of parameters. Our bound assuming that the learning algorithm satisfies certain noise-robustness (adding small amount of label noise causes small degradation in performance) and rationality (getting the wrong label is not better than getting no label at all) conditions that widely (and sometimes provably) hold across many standard architectures. We complement this result with an empirical study\, demonstrating that our bound is non-vacuous for many popular representation-learning based classifiers on CIFAR-10 and ImageNet\, including SimCLR\, AMDIM and BigBiGAN. The talk will not assume any specific background in machine learning\, and should be accessible to a general mathematical audience. Joint work with Gal Kaplun. \n 
URL:https://cmsa.fas.harvard.edu/event/10-28-2020-new-technologies-in-mathematics-seminar/
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-New-Technologies-in-Mathematics-10.28.20.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201029T103000
DTEND;TZID=America/New_York:20201029T120000
DTSTAMP:20260508T090250
CREATED:20240127T023101Z
LAST-MODIFIED:20240127T023101Z
UID:10001492-1603967400-1603972800@cmsa.fas.harvard.edu
SUMMARY:10/29/2020 Quantum Matter Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/10-29-2020-quantum-matter-seminar/
CATEGORIES:Quantum Matter
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201102T103000
DTEND;TZID=America/New_York:20201102T113000
DTSTAMP:20260508T090250
CREATED:20240127T021628Z
LAST-MODIFIED:20240127T021628Z
UID:10001483-1604313000-1604316600@cmsa.fas.harvard.edu
SUMMARY:11/9/2020 Math-Physics Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/11-9-2020-math-physics-seminar/
CATEGORIES:Mathematical Physics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201102T103000
DTEND;TZID=America/New_York:20201102T113000
DTSTAMP:20260508T090250
CREATED:20240127T022854Z
LAST-MODIFIED:20240127T022854Z
UID:10001490-1604313000-1604316600@cmsa.fas.harvard.edu
SUMMARY:11/2/2020 Math-Physics Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/11-2-2020-math-physics-seminar/
CATEGORIES:Mathematical Physics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201103T113000
DTEND;TZID=America/New_York:20201103T123000
DTSTAMP:20260508T090250
CREATED:20240127T022741Z
LAST-MODIFIED:20240127T022741Z
UID:10001489-1604403000-1604406600@cmsa.fas.harvard.edu
SUMMARY:11/3/2020 Computer Science for Mathematicians
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/11-3-2020-computer-science-for-mathematicians/
CATEGORIES:Computer Science for Mathematicians Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201104T140000
DTEND;TZID=America/New_York:20201104T150000
DTSTAMP:20260508T090250
CREATED:20240127T021839Z
LAST-MODIFIED:20240127T021839Z
UID:10001485-1604498400-1604502000@cmsa.fas.harvard.edu
SUMMARY:11/04/2020 RMPT Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/11-04-2020-rmpt-seminar/
CATEGORIES:Random Matrix & Probability Theory Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201104T150000
DTEND;TZID=America/New_York:20201104T160000
DTSTAMP:20260508T090250
CREATED:20240127T021940Z
LAST-MODIFIED:20240515T200835Z
UID:10001486-1604502000-1604505600@cmsa.fas.harvard.edu
SUMMARY:Some exactly solvable models for machine learning via Statistical physics
DESCRIPTION:Speaker: Florent Krzakala\, EPFL \nTitle: Some exactly solvable models for machine learning via Statistical physics \nAbstract: The increasing dimensionality of data in the modern machine learning age presents new challenges and opportunities. The high dimensional settings allow one to use powerful asymptotic methods from probability theory and statistical physics to obtain precise characterizations and develop new algorithmic approaches. Statistical mechanics approaches\, in particular\, are very well suited for such problems. Will give examples of recent works in our group that build on powerful methods of statistical physics of disordered systems to analyze some relevant questions in machine learning and neural networks\, including overparameterization\, kernel methods\, and the behavior gradient descent algorithm in a high dimensional non-convex landscape.
URL:https://cmsa.fas.harvard.edu/event/11-4-2020-new-technologies-in-math/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-New-Technologies-in-Mathematics-11.04.20.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201105T103000
DTEND;TZID=America/New_York:20201105T120000
DTSTAMP:20260508T090250
CREATED:20240127T021509Z
LAST-MODIFIED:20240127T021509Z
UID:10001482-1604572200-1604577600@cmsa.fas.harvard.edu
SUMMARY:11/05/2020 Quantum Matter Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/11-05-2020-quantum-matter-seminar/
CATEGORIES:Quantum Matter
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201106T123000
DTEND;TZID=America/New_York:20201106T150000
DTSTAMP:20260508T090250
CREATED:20240127T021729Z
LAST-MODIFIED:20240127T021729Z
UID:10001484-1604665800-1604674800@cmsa.fas.harvard.edu
SUMMARY:11/6/2020 Strongly Correlated Quantum Materials Lecture
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/11-6-2020-strongly-correlated-quantum-materials-lecture/
CATEGORIES:Strongly Correlated Quantum Materials and High-Temperature Superconductors
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201110T113000
DTEND;TZID=America/New_York:20201110T123000
DTSTAMP:20260508T090250
CREATED:20240127T021030Z
LAST-MODIFIED:20240127T021030Z
UID:10001479-1605007800-1605011400@cmsa.fas.harvard.edu
SUMMARY:11/10/2020 Computer Science for Mathematicians
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/11-10-2020-computer-science-for-mathematicians/
CATEGORIES:Computer Science for Mathematicians Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201111T103000
DTEND;TZID=America/New_York:20201111T120000
DTSTAMP:20260508T090250
CREATED:20240127T020649Z
LAST-MODIFIED:20240127T020649Z
UID:10001476-1605090600-1605096000@cmsa.fas.harvard.edu
SUMMARY:11/11/2020 Quantum Matter Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/11-11-2020-quantum-matter-seminar/
CATEGORIES:Quantum Matter
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201111T150000
DTEND;TZID=America/New_York:20201111T160000
DTSTAMP:20260508T090250
CREATED:20240127T021159Z
LAST-MODIFIED:20240515T200604Z
UID:10001480-1605106800-1605110400@cmsa.fas.harvard.edu
SUMMARY:Towards AI for mathematical modeling of complex biological systems: Machine-learned model reduction\, spatial graph dynamics\, and symbolic mathematics
DESCRIPTION:Speaker: Eric Mjolsness\, Departments of Computer Science and Mathematics\, UC Irvine \nTitle: Towards AI for mathematical modeling of complex biological systems: Machine-learned model reduction\, spatial graph dynamics\, and symbolic mathematics \nAbstract: The complexity of biological systems (among others) makes demands on the complexity of the mathematical modeling enterprise that could be satisfied with mathematical artificially intelligence of both symbolic and numerical flavors. Technologies that I think will be fruitful in this regard include (1) the use of machine learning to bridge spatiotemporal scales\, which I will illustrate with the “Dynamic Boltzmann Distribution” method for learning model reduction of stochastic spatial biochemical networks and the “Graph Prolongation Convolutional Network” approach to course-graining the biophysics of microtubules; (2) a meta-language for stochastic spatial graph dynamics\, “Dynamical Graph Grammars”\, that can represent structure-changing processes including microtubule dynamics and that has an underlying combinatorial theory related to operator algebras; and (3) an integrative conceptual architecture of typed symbolic modeling languages and structure-preserving maps between them\, including model reduction and implementation maps. \n  \n  \n 
URL:https://cmsa.fas.harvard.edu/event/11-11-2020-new-technologies-in-mathematics/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-New-Technologies-in-Mathematics-11.11.20-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201111T150000
DTEND;TZID=America/New_York:20201111T160000
DTSTAMP:20260508T090250
CREATED:20240127T021307Z
LAST-MODIFIED:20240127T021307Z
UID:10001481-1605106800-1605110400@cmsa.fas.harvard.edu
SUMMARY:11/11/2020 RM&PT Seminar
DESCRIPTION:
URL:https://cmsa.fas.harvard.edu/event/11-11-2020-rmpt-seminar/
CATEGORIES:Random Matrix & Probability Theory Seminar
END:VEVENT
END:VCALENDAR