IFDS

IFDS Ideas Forum: Tara Javidi
2021-03-22 13:30 - 14:30 (America/Chicago)
Person: Tara Javadi
Title:
Abstract:
Bio:

SILO: Sebastien Bubeck
2021-03-17 12:30 - 13:30 (America/Chicago)
Title: A law of robustness for two-layers neural networks
UNTIL FURTHER NOTICE: Seminars are virtual. Sign up for the SILO email list to receive the links to each talk at https://groups.google.com/ and browse for silo

IFDS Ideas Forum: Aditya Kumar Akash & Sixu Li
2021-03-15 13:30 - 14:00 (America/Chicago) | Webex
Aditya Kumar Akash & Sixu Li
Title: Deep fusion through Wasserstein barycenters in TLp spaces
Abstract:

ML-Opt @ UWash: Vincent Roulet
2021-03-12 13:30 - 14:30 (America/Chicago)
Title: Global convergence of first-order methods for nonlinear control problems
Abstract:

SILO: Yuanzhi Li
2021-03-10 13:00 - 13:30 (America/Chicago)
Title: TBD
UNTIL FURTHER NOTICE: Seminars are virtual. Sign up for the SILO email list to receive the links to each talk at https://groups.google.com/ and browse for silo

SILO: Zhao Song
2021-03-10 12:30 - 13:30 (America/Chicago)
Title: Faster Optimization: From linear programming to semidefinite programming
Abstract: Many important real-life problems, in both convex and non-convex settings, can be solved using path-following optimization methods. The running time of optimization algorithms is typically governed by two components — the number of iterations and the cost-per-iteration. For decades, the vast majority of research effort was dedicated to improving the number of iterations required for convergence. A recent line of work of ours shows that the cost-per-iteration can be dramatically improved using a careful combination of dynamic data structures with `robust’ variants of the optimization method. A central ingredient is the use of randomized linear algebra for dimensionality reduction (e.g., linear sketching) for fast maintenance of dynamic matrix problems. This framework recently led to many breakthroughs on decade-old optimization problems.
In this talk, I will present the framework underlying these breakthroughs, focusing on faster algorithms for linear programming and semidefinite programming. We will first present how to use the above idea to speed up general LP solvers by providing an n^omega + n^{2+1/18} time algorithm. We then show how to apply similar ideas to SDP solvers by providing an n^omega + n^{2+1/4} time algorithm. For the current omega = 2.373, we can solve LP and SDP as fast as solving linear systems.
This is a joint work with
Baihe Huang (undergraduate at Peking University),
Shunhua Jiang, Runzhou Tao, Hengjie Zhang (Ph.D. at Columbia University),
Omri Weinstein (Professor at Columbia University)
LP paper : https://arxiv.org/abs/2004.07470
SDP paper : https://arxiv.org/abs/2101.08208

UNTIL FURTHER NOTICE: Seminars are virtual. Sign up for the SILO email list to receive the links to each talk at https://groups.google.com/ and browse for silo

IFDS Ideas Forum: Nicolas Garcia Trillos
2021-03-08 13:30 - 14:30 (America/Chicago)
Person: Nicolas Garcia Trillos
Title: Adversarial classification, optimal transport, and geometric evolutions
Abstract:
Bio: I am currently an Assistant Professor in the Department of Statistics at the University of Wisconsin Madison. From 2015 – 2018, I was a Prager Assistant Professor (postdoctoral position) at Brown University. I finished my Ph.D in mathematics at Carnegie Mellon University in 2015; my adviser was Dejan Slepčev. I received my Bachelor’s degree in mathematics from Universidad de Los Andes in Bogotá, Colombia, in 2010.

IFDS All-Hands: Yang Liu
2021-03-05 13:30 - 14:30 (America/Los_Angeles)
Prof. Yang Liu, Computer Science Dept, UC Santa Cruz
Title: Consequential Machine Learning
Abstract: Recent findings cautioned the potential unfairness issues that can arise when deploying a machine learning model. Correspondingly, treatments have been proposed to add fairness guarantees when training such statistical models. An often overlooked question is “what after?” One can discover ways to improve the model after deployment, and after receiving feedback from human subjects that saw these algorithmic treatments. But the unfortunate fact is once a model is deployed, it will start to impact a later decision and the society for a long period of time. In this talk, I am going to introduce some of the recent works that initialize our attempt to understand the consequences of the deployment of a machine learning algorithm. We will first show that an appeared-to-be fair algorithm might not necessarily help reduce disparities among societal groups that we aim to protect. The long-term impacts of a sequence of deployments of machine learning models will depend on the induced dynamics of the underlying populations’ qualification. Then I discuss how to use the design and deployment of machine learning to induce proper dynamics by offering human agents actionable recourse to improve their qualifications.
All Hands titles and abstracts are tentative, as of the posting date.

SILO: Tuo Zhao
2021-03-03 12:30 - 13:30 (America/Chicago)

IFDS Ideas Forum: Julian Katz-Samuels
2021-03-01 13:30 - 14:30 (America/Chicago)
Person: Julian Katz-Samuels
Title:
Abstract:
Bio:

ML-Opt @ UWash: Andrew Wagenmaker
2021-02-26 13:30 - 14:30 (America/Los_Angeles)
Title:

Abstract:

SILO: Jose Blanchet
2021-02-24 12:30 - 13:30 (America/Chicago)
Title: TBD

IFDS Ideas Forum: Blake Mason
2021-02-22 13:30 - 14:30 (America/Chicago)
PERSON: Blake Mason
TITLE: Metric Learning from Labelled Data is Really Hard
ABSTRACT:
BIO:

ML-Opt @ UWash: Omid Sadeghi
2021-02-19 13:30 - 14:30 (America/Chicago)

SILO: Tara Javidi
2021-02-17 12:30 - 13:30 (America/Chicago)
Generalization to Natural Out-of-Distribution Inputs
UNTIL FURTHER NOTICE: Seminars are virtual. Sign up for the SILO email list to receive the links to each talk at https://groups.google.com/ and browse for silo

IFDS Ideas Forum: Steve Wright
2021-02-15 13:30 - 14:30 (America/Chicago)
PERSON: Steve Wright
TITLE: Discussion: Zero-Loss NN training
ABSTRACT:
BIO:

ML-Opt @ UWash: Postponed-TBD
2021-02-12 13:30 - 14:30 (America/Chicago)
This talk will be rescheduled for a later date.
Title: Convergence of the empirical superquantile for risk-sensitive machine learning
Abstract: I’ll present some background in risk-sensitive learning + Theorem 2.1 of this paper (using ML nomenclature)

SILO: Hamed Hasani
2021-02-10 12:30 - 13:30 (America/Chicago)
Two Facets of Learning Robust Models: Fundamental Limits and
UNTIL FURTHER NOTICE: Seminars are virtual. Sign up for the SILO email list to receive the links to each talk at https://groups.google.com/ and browse for silo

IFDS Ideas Forum: Miaoyan Wang
2021-02-08 13:30 - 14:30 (America/Chicago)
PERSON: Miaoyan Wang
TITLE: Beyond the Signs: Nonparametric Tensor Completion via Sign Series
ABSTRACT:
BIO: I am an assistant professor in the Department of Statistics at the University of Wisconsin-Madison since late 2018. I am also an affiliate of the Institute for Foundations of Data Science (IFDS) at Wisconsin Institute for Discovery (WID). My research is in statistical machine learning with a particular focus on matrix/tensor data analysis. Other interests include statistical applications in genetics and neuroimaging.
Prior to UW-Madision, I was a postdoc in Computer Science (EECS Department) at UC Berkeley and a Simons Math+X postdoc at University of Pennsylvania. I received my PhD in Statistics from the University of Chicago and BS in Mathematics from Fudan University.

IFDS All-Hands: Zaid Harchaoui
2021-02-05 13:30 - 14:30 (America/Los_Angeles)
Zaid Harchaoui, Associate Professor, Statistics
Title: On statistical estimation, signal denoising, and convex optimization
Abstract: We revisit the classical statistical problem of adaptive discrete-time signal denoising. Conventional nonparametric statistics and signal processing approaches rely on strong structural assumptions on the signal set. The approach we present, inspired by the seminal works from Ibragimov-Khasminskii and later Donoho, takes a different view and focuses instead on statistical estimators amenable to convex optimization. We show that such estimators possess better statistical properties than conventional ones. In particular, under an assumption of approximate shift-invariance, the proposed estimators enjoy l2-loss oracle inequalities. We show how to implement them using optimal first-order methods and highlight interesting statistical-computational trade-offs. Joint work with D. Ostrovskii, A. Juditsky, A. Nemirovski.

SILO: Yao Xie
2021-02-03 12:30 - 13:30 (America/Chicago)
Statistical inference on spatio-temporal networks
UNTIL FURTHER NOTICE: Seminars are virtual. Sign up for the SILO email list to receive the links to each talk at https://groups.google.com/ and browse for silo

ML-Opt @ UWash: Ronak Mehta
2021-01-29 13:30 - 14:30 (America/Chicago)

IFDS Ideas Forum: Xuexhou Zhang
2021-01-25 13:30 - 14:30 (America/Chicago) | Webex
Title: Statistical Robustness in Reinforcement Learning
Abstract:
Xuezhou Zhang (Computer Sciences), advised by Jerry Zhu (Computer Sciences) and Kevin Jamieson (U of Washington), works on adaptivity and robustness in sequential decision making. His recent work focuses on designing reinforcement learning framework that learns from a diverse source of teaching signals and learns robustly in the presence of data corruptions.

ML-Opt @ UWash: Lang Liu
2021-01-22 13:30 - 14:30 (America/Los_Angeles)

ML-Opt @ U Washington: Yue Yu
2021-01-15 13:30 - 14:30 (America/Los_Angeles)
Title: Proportional-integral projected gradient method for model predictive control
Abstract:

IFDS Ideas Forum
2013-03-27 14:00 (America/Chicago)
Current research

Sushrut Karmalkar
2026-08-16 06:23 (America/Chicago)

Jitian Zhao / Zhexuan Liu
2026-08-16 06:23 (America/Chicago)

Thanasis Pittas
2026-08-16 06:23 (America/Chicago)

William Powell / Andrew Lowy
2026-08-16 06:23 (America/Chicago)
