IFDS

SILO: Yiqiao Zhong
2022-12-07 12:30 (America/Chicago)
Yiqiao Zhong

IFDS Ideas Forum: Jenny Wei, Ying Fan
2022-12-05 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

IFDS All-Hands
2022-12-02 13:30, 2022-11-04 13:30 (America/Los_Angeles) | University of Washington, Seattle, 185 E Stevens Way NE, Seattle, WA, 98195-2350, United States

SILO: Kirthevasan Kandasamy
2022-11-30 12:30 (America/Chicago)
Kirthevasan Kandasamy

IFDS Ideas Forum: Hao Yan
2022-11-28 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

MLOpt:
2022-11-25 13:30, 2022-11-18 13:30, 2022-11-11 13:30, 2022-10-28 13:30 (America/Los_Angeles) | University of Washington, Seattle, 185 E Stevens Way NE, Seattle, WA, 98195-2350, United States

SILO: Yongyi Guo
2022-11-23 12:30 (America/Chicago)
Yongyi Guo

IFDS Ideas Forum: Yinglun Zhu
2022-11-21 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

SILO: Sameer Deshpande
2022-11-16 12:30 (America/Chicago)
Sameer Deshpande

IFDS Ideas Forum: Gokcan Tatli, Jeremy McMahan
2022-11-14 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
TBD, Attacks on RL

IFDS Special Seminar (online only): Weijie Su
2022-11-10 12:30 - 13:30 (America/Chicago) | Zoom
(in conjunction with Neural Collapse reading group)

SILO: Yuxin Chen
2022-11-09 12:30 (America/Chicago)
Yuxin Chen

IFDS Ideas Forum: Vivak Patel
2022-11-07 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Counter Examples for (Stochastic) Gradient Descent

SILO: Cliff Stein
2022-11-02 12:30 (America/Chicago)
Cliff Stein

IFDS Ideas Forum: Yuetian Luo, Nicolás García Trillos
2022-10-31 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Nonconvex Matrix Factorization is Geodesically Convex: GlobalLandscape Analysis for Fixed-rank Matrix Optimization From a Riemannian Perspective

SILO: Cynthia Rudin
2022-10-26 12:30 (America/Chicago)
Cynthia Rudin

MLOpt: Lang Liu
2022-10-21 13:30 - 14:30 (America/Los_Angeles) | University of Washington, Seattle, 185 E Stevens Way NE, Seattle, WA, 98195-2350, United States
Speaker: Lang Liu
Title: Non-Asymptotic Analysis of M-Estimation for Statistical Learning and Inference under Self-Concordance
Abstract: 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.

SILO: Kimin Lee
2022-10-19 12:30 (America/Chicago)
Kimin Lee

IFDS Ideas Forum: Aadirupa Saha
2022-10-17 12:30 - 13:30 (America/Chicago)
Dueling-Opt: Convex Optimization with Relative Feedback
(TTI-Chicago)

MLOpt @ UWash: Steve Mussmann
2022-10-14 13:30 - 14:30 (America/Los_Angeles) | University of Washington, Seattle, 185 E Stevens Way NE, Seattle, WA, 98195-2350, United States
Title: Data pruning via Machine Teaching
Abstract: 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.
Virtual option available via IFDS mailing list.

SILO: Jong Wook Kim
2022-10-12 12:30 (America/Chicago)
Jong Wook Kim

IFDS Ideas Forum: Jordan Ellenberg
2022-10-10 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Why pure mathematicians should care about machine learning

SILO: Pedro Morgado
2022-10-05 12:30 (America/Chicago)
Pedro Morgado

IFDS Ideas Forum: Jakwang Kim and Nicolás García Trillos
2022-10-03 12:30 - 13:30 (America/Chicago) | McArdle 1109
The multimarginal optimal transport formulation of adversarial multiclass classification

SILO: Chris Rycroft
2022-09-28 12:30 (America/Chicago)
Chris Rycroft

SILO: Tom Goldstein
2022-09-21 12:30 (America/Chicago)
Tom Goldstein
