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

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:

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

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: Ronak Mehta
2021-01-29 13:30 - 14:30 (America/Chicago)

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

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.

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.

SILO: Hamed Hasani
2021-02-10 12:30 - 13:30 (America/Chicago)
Two Facets of Learning Robust Models: Fundamental Limits and
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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)

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:

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

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

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:

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

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

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

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

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.

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.

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

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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

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:

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:

SILO: Sebastien Bubeck
2021-03-17 12:30 - 13:30 (America/Chicago)
Title: A law of robustness for two-layers neural networks
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IFDS Ideas Forum: Tara Javidi
2021-03-22 13:30 - 14:30 (America/Chicago)
Person: Tara Javadi
Title:
Abstract:
Bio:

SILO: Alon Orlitsky
2021-03-24 08:00 - 17:00 (America/Chicago)

IFDS Ideas Forum: Jeffrey Covington
2021-03-29 13:30 - 14:00 (America/Chicago) | Webex
Title:
Abstract:
Jeffrey Covington (Mathematics), advised by Nan Chen (Mathematics) and Sebastien Roch (Mathematics), works on data assimilation for model state estimation and prediction. His primary focus is on models with nonlinear and non-Gaussian features, which present problems for traditional data assimilation techniques. Currently he is working on developing techniques for Lagrangian data assimilation problems, which typically involve high-dimensionality and strong nonlinear interactions.

SILO: Jean Honorio
2021-03-31 12:30 - 13:30 (America/Chicago)

IFDS All-Hands: Chaobing Song & Xuezhou Zhang
2021-04-02 13:30 - 14:30 (America/Los_Angeles)
Speaker 1: Dr. Chaobing Song, IFDS Postdoctoral Scholar at U Wisconsin (advised by Prof.s Jelena Diakonikolas and Steve Wright)

Title: Closing Convergence Gaps for Both Smooth and Nonsmooth Convex Finite-Sums

Abstract: In the Foundation of Data Science, optimization problems with a finite-sum structure widely exist, such as the classical empirical risk minimization problem and deep neural network. In the past decade, one main concern in the study of first-order methods is to study how the finite-sum structure influences optimization efficiency and scalability. In this talk, based on my work (https://arxiv.org/abs/2006.10281, https://arxiv.org/abs/2102.13643), I will talk about a consistent approach based on dual averaging and a particularly designed initialization strategy to close convergence gaps for smooth convex finite-sums and nonsmooth convex finite-sums. For the first time, the proposed VRADA algorithm (https://arxiv.org/abs/2006.10281) matches the lower bounds of all the three regimes for smooth convex finite-sums; for the first time, the proposed VRPDA^2 algorithm (https://arxiv.org/abs/2102.13643) shows an O(n) improvement theoretically over existing deterministic methods and stochastic primal-dual coordinate methods, where n is the number of data samples. Both algorithms also have good empirical performance.

Speaker 2: Xuezhou Zhang, IFDS RA (advised by Prof. Jerry Zhu)

Title: Statistical Robustness in Reinforcement Learning

Abstract: Traditional robust statistics studies the problem of statistical estimation under data contamination. In this talk, we extend this investigation to decision-making problems. In the first half of the talk, I will illustrate the concept and unique challenge of robust Decision making using a multi-armed bandit as an example. In the second half of the talk, I will present some of the lower and upper-bound results we have already established, and discuss several open directions. This talk is partially based on our recent paper [https://arxiv.org/pdf/2102.05800.pdf].

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Speaker Bios:

Dr. Chaobing Song: I am a postdoc researcher at University of Wisconsin-Madison with Prof. Jelena Diakonikolas and Prof. Stephen J Wright. My research interests include optimization and machine learning. I obtained my Ph.D. degree at Tsinghua University in 2020 supervised by Prof. Yi Ma from University of California, Berkeley (who is an affiliate professor at Tsinghua-Berkeley Shenzhen Institute, Tsinghua University). I spent two wonderful years at Berkeley and finished my Ph.D. thesis there. The main focus of my current research is to apply a general, powerful and concise framework [arXiv] to optimization problems in machine learning. The development based on this framework substantially improves complexity results in many well-known settings, such as the setting of nonsmooth convex finite-sums in this talk.

Xuezhou Zhang: Xuezhou is a Ph.D. candidate in the computer sciences department at the University of Wisconsin-Madison, advised by Professor Jerry Zhu. Before coming to Madison, he obtained a Bachelor of Applied Mathematics degree from UCLA. His research interests lie in machine learning and reinforcement learning, and his recent research focuses on designing reinforcement learning algorithms that learn more adaptively and robustly in non-stochastic environments.

These talks are remote via zoom. Please contact the organizer if you need the link.
All-Hands titles and abstracts are tentative, as of the posting date.
