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X-WR-CALDESC:Events for IFDS
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DTSTART:20220313T080000
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20221003T123000
DTEND;TZID=America/Chicago:20221003T133000
DTSTAMP:20260418T053452
CREATED:20221018T161422Z
LAST-MODIFIED:20221018T163940Z
UID:2278-1664800200-1664803800@ifds.info
SUMMARY:IFDS Ideas Forum: Jakwang Kim and Nicolás García Trillos
DESCRIPTION:The multimarginal optimal transport formulation of adversarial multiclass classification
URL:https://ifds.info/event/10032022/
LOCATION:McArdle 1109
CATEGORIES:IFDS Ideas Forum
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20221005T123000
DTEND;TZID=America/Chicago:20221005T123000
DTSTAMP:20260418T053452
CREATED:20221012T172929Z
LAST-MODIFIED:20221012T174603Z
UID:2223-1664973000-1664973000@ifds.info
SUMMARY:SILO: Pedro Morgado
DESCRIPTION:Pedro Morgado
URL:https://ifds.info/event/100522/
LOCATION:WI
CATEGORIES:SILO
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20221010T123000
DTEND;TZID=America/Chicago:20221010T133000
DTSTAMP:20260418T053452
CREATED:20221018T161422Z
LAST-MODIFIED:20221018T163828Z
UID:2279-1665405000-1665408600@ifds.info
SUMMARY:IFDS Ideas Forum: Jordan Ellenberg
DESCRIPTION:Why pure mathematicians should care about machine learning
URL:https://ifds.info/event/10102022/
LOCATION:Orchard View Room\, 330 N. Orchard Street\, 3rd Floor NE\, Madison\, Wisconsin\, 53715\, United States
CATEGORIES:IFDS Ideas Forum
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20221012T123000
DTEND;TZID=America/Chicago:20221012T123000
DTSTAMP:20260418T053452
CREATED:20221012T172929Z
LAST-MODIFIED:20221012T174528Z
UID:2222-1665577800-1665577800@ifds.info
SUMMARY:SILO: Jong Wook Kim
DESCRIPTION:Jong Wook Kim
URL:https://ifds.info/event/101222-2/
LOCATION:WI
CATEGORIES:SILO
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20221014T133000
DTEND;TZID=America/Los_Angeles:20221014T143000
DTSTAMP:20260418T053452
CREATED:20221012T175304Z
LAST-MODIFIED:20221018T180930Z
UID:2264-1665754200-1665757800@ifds.info
SUMMARY:MLOpt @ UWash: Steve Mussmann
DESCRIPTION:Title: Data pruning via Machine Teaching \nAbstract: In this talk\, I discuss the problem of data pruning: given a fully labeled dataset and a training procedure\, select a subset such that training on that subset yields approximately the same test performance as training on the full dataset. I present our algorithm\, inspired by recent work in machine teaching. Through experimental results and theoretical analysis\, we find that machine teaching is an effective paradigm for data pruning. \nVirtual option available via IFDS mailing list.
URL:https://ifds.info/event/mlopt-uwash-steve-mussmann/
LOCATION:University of Washington\, Seattle\, 185 E Stevens Way NE\, Seattle\, WA\, 98195-2350\, United States
CATEGORIES:MLOpt@UWash
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20221017T123000
DTEND;TZID=America/Chicago:20221017T133000
DTSTAMP:20260418T053452
CREATED:20221018T161426Z
LAST-MODIFIED:20221018T163920Z
UID:2280-1666009800-1666013400@ifds.info
SUMMARY:IFDS Ideas Forum: Aadirupa Saha
DESCRIPTION:Dueling-Opt: Convex Optimization with Relative Feedback \n(TTI-Chicago)
URL:https://ifds.info/event/10172022/
LOCATION:WI
CATEGORIES:IFDS Ideas Forum
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20221019T123000
DTEND;TZID=America/Chicago:20221019T123000
DTSTAMP:20260418T053452
CREATED:20221012T172928Z
LAST-MODIFIED:20221012T175649Z
UID:2221-1666182600-1666182600@ifds.info
SUMMARY:SILO: Kimin Lee
DESCRIPTION:Kimin Lee
URL:https://ifds.info/event/101222/
LOCATION:WI
CATEGORIES:SILO
ATTACH;FMTTYPE=image/png:https://ifds.info/wp-content/uploads/2022/10/SILO-1024x683-1-e1665597390709.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20221021T133000
DTEND;TZID=America/Los_Angeles:20221021T143000
DTSTAMP:20260418T053452
CREATED:20221018T155613Z
LAST-MODIFIED:20221018T180956Z
UID:2269-1666359000-1666362600@ifds.info
SUMMARY:MLOpt: Lang Liu
DESCRIPTION:Speaker: Lang Liu\nTitle: Non-Asymptotic Analysis of M-Estimation for Statistical Learning and Inference under Self-Concordance \nAbstract: In this talk\, I discuss the problem of M-estimation for statistical learning and inference. It is well-known from the classical asymptotic theory that the properly centered and normalized estimator has a limiting Gaussian distribution with a sandwich covariance. I first establish a finite-sample bound for the estimator\, characterizing its asymptotic behavior in a non-asymptotic fashion. An important feature of the bound is that its dimension dependency is characterized by the effective dimension — the trace of the limiting sandwich covariance — which can be much smaller than the parameter dimension in some regimes. I then illustrate how the bound can be used to obtain a confidence set whose shape is adapted to the local curvature of the population risk. In contrast to previous work which relied heavily on the strong convexity of the learning objective\, I only assume the Hessian is lower bounded at optimum and allow it to gradually become degenerate. This property is formalized by the notion of self-concordance originating from convex optimization. Finally\, I apply these techniques to semi-parametric estimation and derive state-of-the-art finite-sample bounds for double machine learning and orthogonal statistical learning.
URL:https://ifds.info/event/mlopt-non-asymptotic-analysis-of-m-estimation-for-statistical-learning-and-inference-under-self-concordance/
LOCATION:University of Washington\, Seattle\, 185 E Stevens Way NE\, Seattle\, WA\, 98195-2350\, United States
CATEGORIES:MLOpt@UWash
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20221026T123000
DTEND;TZID=America/Chicago:20221026T123000
DTSTAMP:20260418T053452
CREATED:20221012T172928Z
LAST-MODIFIED:20221012T181652Z
UID:2220-1666787400-1666787400@ifds.info
SUMMARY:SILO: Cynthia Rudin
DESCRIPTION:Cynthia Rudin
URL:https://ifds.info/event/102622/
LOCATION:WI
CATEGORIES:SILO
ATTACH;FMTTYPE=image/png:https://ifds.info/wp-content/uploads/2022/10/SILO-1024x683-1-e1665597390709.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20221028T133000
DTEND;TZID=America/Los_Angeles:20221028T133000
DTSTAMP:20260418T053452
CREATED:20221018T165642Z
LAST-MODIFIED:20221018T165731Z
UID:2316-1666963800-1666963800@ifds.info
SUMMARY:MLOpt:
DESCRIPTION:
URL:https://ifds.info/event/mlopt-2/
LOCATION:University of Washington\, Seattle\, 185 E Stevens Way NE\, Seattle\, WA\, 98195-2350\, United States
CATEGORIES:MLOpt@UWash
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20221031T123000
DTEND;TZID=America/Chicago:20221031T133000
DTSTAMP:20260418T053452
CREATED:20221018T161426Z
LAST-MODIFIED:20221018T163802Z
UID:2281-1667219400-1667223000@ifds.info
SUMMARY:IFDS Ideas Forum: Yuetian Luo\, Nicolás García Trillos
DESCRIPTION:Nonconvex Matrix Factorization is Geodesically Convex: GlobalLandscape Analysis for Fixed-rank Matrix Optimization From a Riemannian Perspective \n 
URL:https://ifds.info/event/10312022/
LOCATION:Orchard View Room\, 330 N. Orchard Street\, 3rd Floor NE\, Madison\, Wisconsin\, 53715\, United States
CATEGORIES:IFDS Ideas Forum
END:VEVENT
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