IFDS Ideas Forum:
2022-04-11 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Speaker: Vivak Patel

IFDS Ideas Forum
2022-04-11 14:00 (America/Chicago)
Not sure yet, but I will come up with something

SILO: Mihaela van der Schaar
2022-04-13 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

IFDS Ideas Forum: RA talk
2022-04-18 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Speaker: Jiaxin Hu

Speaker: Yuchen Zeng

IFDS Ideas Forum
2022-04-18 14:00, 2022-04-25 14:00 (America/Chicago)
IFDS RA talk

SILO: Misha Belkin
2022-04-20 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

IFDS Ideas Forum: RA Talk
2022-04-25 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Speaker: Sijia Fang

Speaker: Shubham Bharti

SILO: Dan Roy
2022-04-27 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

IFDS Ideas Forum: RA Talk
2022-05-02 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Speaker: Liu Yang

Speaker: Nayoung Lee

IFDS Ideas Forum
2022-05-02 14:00 (America/Chicago)
Not sure yet

SILO: Qing Liu
2022-05-04 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

Solution Structure Utilization for Efficient Optimization and Large-scale Machine Learning
2022-05-09 12:30 - 13:30 (America/Chicago)
Speaker: Ching-pei Lee

IFDS Ideas Forum
2022-05-09 14:00 (America/Chicago)
Solution Structure Utilization for Efficient Optimization and Large-scale Machine Learning

PIMS-IFDS-NSF Summer School in Optimal Transport
2022-06-20 00:00 - 2022-07-01 23:59 (America/Chicago) | University of Washington, Seattle, 185 E Stevens Way NE, Seattle, WA, 98195-2350, United States

IFDS Workshop on Distributional Robustness in Data Science
2022-08-04 00:00 - 2022-08-06 23:59 (America/Chicago) | University of Washington, Seattle, 185 E Stevens Way NE, Seattle, WA, 98195-2350, United States

SILO: Peter Stone
2022-09-07 12:30 (America/Chicago)
Peter Stone

IFDS Special Interest Group Bazaar
2022-09-12 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Jerry Zhu, Greg Canal, Keith Levin

SILO: Neural Collapse in Deep Net Training
2022-09-14 12:30 (America/Chicago)
XY Han

IFDS Ideas Forum: Meet the Postdocs!
2022-09-19 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
All eight postdocs

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

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

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

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

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.

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

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

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.
