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X-WR-CALDESC:Events for IFDS
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TZID:America/Chicago
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DTSTART:20240310T080000
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DTSTART:20241103T070000
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DTSTART;TZID=America/Chicago:20240205T123000
DTEND;TZID=America/Chicago:20240205T133000
DTSTAMP:20260530T071403
CREATED:20240318T213252Z
LAST-MODIFIED:20240318T213252Z
UID:2892-1707136200-1707139800@ifds.info
SUMMARY:Towards a new toolbox of optimal statistical primitives
DESCRIPTION:Abstract: Given society’s increasing reliance on data\, its collection and processing into useful information is a technical problem of growing focus\, and perhaps paradoxically\, a critical bottleneck in many data science and machine learning applications. My research focuses on designing algorithms that push the limits of both statistical efficiency and computational efficiency. In particular\, my work tackles the divide between the theory and practice of data science\, which exists even for the most basic statistical problems including mean and (co)variance estimation. Conventional methods such as the sample mean\, while supported by theoretical results under strong assumptions\, are often brittle in the presence of extreme data points. To counter such deficiencies\, practitioners often use ad-hoc and unprincipled “outlier removal” heuristics\, revealing a marked gap between the theory and practice even for these fundamental problems. \nIn this talk\, I will describe my work towards building a new toolbox of optimal statistical primitives\, bridging the theory-practice divide. I will specifically highlight 3 works: A) constructing a statistically-optimal and computationally-efficient 1-dimensional mean estimator\, whose estimation error is optimal even in the leading multiplicative constant\, under bare minimum distributional assumptions\, B) a rather different but also optimal mean estimator for the “very high-dimensional” regime\, and C) a recent result on robustly clustering Gaussian mixtures based on their covariances even in the presence of adversarial data corruption. To conclude the talk\, I will discuss my vision for the new theory and toolbox\, serving as a blueprint for my long-term future research. \nBio: Jasper Lee is a postdoctoral research associate at the University of Wisconsin-Madison\, mentored by Ilias Diakonikolas in the Department of Computer Sciences\, and also affiliated with the Institute for Foundations of Data Science. He completed his PhD at Brown University\, advised by Paul Valiant. \nHis research interests are broadly in the foundations of data science\, aiming to design practical\, data-efficient and computationally-efficient algorithms for a variety of statistical applications. \nHis work is partially supported by a Croucher Fellowship for Postdoctoral Research.
URL:https://ifds.info/event/towards-a-new-toolbox-of-optimal-statistical-primitives/
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:20240212T123000
DTEND;TZID=America/Chicago:20240212T133000
DTSTAMP:20260530T071403
CREATED:20240318T213417Z
LAST-MODIFIED:20240318T213417Z
UID:2894-1707741000-1707744600@ifds.info
SUMMARY:Theoretical exploration of foundation model adaption methods
DESCRIPTION:Speaker: Kangwook Lee
URL:https://ifds.info/event/theoretical-exploration-of-foundation-model-adaption-methods/
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:20240219T123000
DTEND;TZID=America/Chicago:20240219T133000
DTSTAMP:20260530T071403
CREATED:20240318T213630Z
LAST-MODIFIED:20240318T213656Z
UID:2897-1708345800-1708349400@ifds.info
SUMMARY:A good score does not lead to a good generative model
DESCRIPTION:Speaker: Sixu Li \nAbstract: Score-based Generative Models (SGMs) is one leading method in generative modeling\, renowned for their ability to generate high-quality samples from complex\, high-dimensional data distributions. The method enjoys empirical success and is supported by rigorous theoretical convergence properties. In particular\, it has been shown that SGMs can generate samples from a distribution that is close to the ground-truth if the underlying score function is learned well\, suggesting the success of SGM as a generative model. We provide a counter-example in this paper. Through the sample complexity argument\, we provide one specific setting where the score function is learned well. Yet\, SGMs in this setting can only output samples that are Gaussian blurring of training data points\, mimicking the effects of kernel density estimation. The finding resonates a series of recent finding that reveal that SGMs can demonstrate strong memorization effect and fail to generate. This is joint with Shi Chen and Qin Li. 
URL:https://ifds.info/event/a-good-score-does-not-lead-to-a-good-generative-model/
LOCATION:WID 1145\, 330 N Orchard Street\, Madison\, WI\, 53715\, United States
CATEGORIES:IFDS Ideas Forum
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20240226T123000
DTEND;TZID=America/Chicago:20240226T133000
DTSTAMP:20260530T071403
CREATED:20240318T213825Z
LAST-MODIFIED:20240318T213825Z
UID:2900-1708950600-1708954200@ifds.info
SUMMARY:Prelimit coupling and steady-state convergence of constant-stepsize nonsmooth contractive SA
DESCRIPTION:Speaker: Yixuan Zhang \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAbstract:  \nMotivated by Q-learning\, we study nonsmooth contractive stochastic approximation (SA) with constant stepsize. We focus on two important classes of dynamics: 1) nonsmooth contractive SA with additive noise\, and 2) synchronous and asynchronous Q-learning\, which features both additive and multiplicative noise. For both dynamics\, we establish weak convergence of the iterates to a stationary limit distribution in Wasserstein distance. Furthermore\, we propose a prelimit coupling technique for establishing steady-state convergence and characterize the limit of the stationary distribution as the stepsize goes to zero. Using this result\, we derive that the asymptotic bias of nonsmooth SA is proportional to the square root of the stepsize\, which stands in sharp contrast to smooth SA. This bias characterization allows for the use of Richardson-Romberg extrapolation for bias reduction in nonsmooth SA.
URL:https://ifds.info/event/prelimit-coupling-and-steady-state-convergence-of-constant-stepsize-nonsmooth-contractive-sa/
LOCATION:WID 1145\, 330 N Orchard Street\, Madison\, WI\, 53715\, United States
CATEGORIES:IFDS Ideas Forum
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