IFDS

IFDS Ideas Forum
2023-03-06 12:30 - 13:30, 2023-02-27 12:30 - 13:30 (America/Chicago)
(WID 4130)

IFDS Ideas Forum
2023-02-20 12:30 - 13:30, 2023-02-13 12:30 - 13:30 (America/Chicago)
ICML Bazaar

IFDS Ideas Forum
2023-02-06 12:30 - 13:30 (America/Chicago)
Announcements, Several topics of current research

MLOpt:
2022-12-16 13:30, 2022-12-09 13:30, 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: 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

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)
