Online Seminar on Mathematical Foundations of Data Science

A weekly online seminar on random topics on mathematical foundations of machine learning, statistics and optimization Sponsored by Two Sigma

Our sponsor, Two Sigma, is soliciting nominations for the Two Sigma PhD Fellowship Program.

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To ask the speaker a question, please use the Q&A function in Zoom to type your questions. The moderator (Zhuoran Yang) will collect questions through the talk and ask the questions after the talk.

Next Speaker: David Morton, Northwestern University

Date/Time: Friday, 6/11, 11am EDT [Zoom Link]

Title: Distributionally Robust Two- and Multi-Stage Stochastic Programming


Abstract: Distributionally robust optimization allows decisions from a stochastic optimization model to hedge against a range of distributions, characterized by an ambiguity set. We first study two-stage stochastic programs with linear recourse and formulate distributionally robust models that vary in how the ambiguity set is built, using the Wasserstein distance and an optimal quadratic transport distance. We consider both unbounded and bounded support sets, and provide guidance regarding which models are meaningful in the sense of yielding robust first-stage decisions. Second, we consider a multi-stage stochastic linear program that lends itself to solution by stochastic dual dynamic programming (SDDP). In this context, we again consider a distributionally robust variant of the model. We describe a computationally tractable variant of SDDP to handle this model using the Wasserstein distance. This is joint work with Daniel Duque and Sanjay Mehrotra.


Bio: David Morton is the David A. and Karen Richards Sachs Professor and Department Chair in Industrial Engineering and Management Sciences at Northwestern University. Prior to joining Northwestern, he served on the faculty at the University of Texas at Austin and as a postdoctoral fellow at the Naval Postgraduate School. He research interests include stochastic optimization for decision making under uncertainty. He worked as a Fulbright Scholar at Charles University in Prague, was a finalist for the EURO Excellence in Practice Paper Prize, and is an INFORMS fellow. He served as the INFORMS Optimization Chair, and he was a recipient of NSF PECASE award.

Upcoming Speakers

David Morton

Northwestern University

Title: Distributionally Robust Two- and Multi-Stage Stochastic Programming

Date/Time: Friday June 11th 11am EDT

John Lafferty

Yale University

Title: TBA

Date/Time: Friday July 2nd 11am EDT

Melvyn Sim

National University of Singapore

Title: TBA

Date/Time: Friday July 9th 11am EDT

Organizers

If you have any questions, please feel free to contact the organizers.

Ethan X. Fang, Niao He, Junwei Lu, Zhaoran Wang, Zhuoran Yang, Tuo Zhao

Ethan X. Fang

Penn State University

Niao He

ETH Zurich

Junwei Lu

Harvard University

Zhaoran Wang

Northwestern University

Zhuoran Yang

Princeton University

Tuo Zhao

Georgia Institute of Technology

Previous Talks (Including Recordings)

Adrian S. Lewis

Cornell University

Title: Smoothness in Nonsmooth Optimization

Date/Time: July 7th 3pm EDT

Recording

Samory Kpotufe

Columbia University

Title: Some Recent Insights on Transfer-Learning

Date/Time: July 21th 3pm EDT

Recording

Eric J. Tchetgen Tchetgen

The Wharton School, UPenn

Title: TBA

Date/Time: July 28th 3pm EDT

Recording

John Urschel

MIT

Title: TBA

Date/Time: August 4th 3pm EDT

Michael I. Jordan

UC Berkeley

Title: Optimization with Momentum: Dynamical, Variational, and Symplectic Perspectives

Date/Time: August 11th 3pm EDT

Recording

Devavrat Shah

MIT

Title: Synthetic Interventions

Date/Time: August 18th 3pm EDT

Recording

James Robins

Harvard University

Title: On Nearly Assumption-Free Tests of Nominal Confidence Interval Coverage for Causal Parameters Estimated by Machine Learning

Date/Time: September 1st 3pm EDT

Recording

Lin Xiao

Microsoft Research

Title: Statistical Optimization Methods for Machine Learning

Date/Time: Tuesday Sep 15th 3pm EDT

Recording

Jim Dai

Cornell University

Title: Queueing Network Controls via Deep Reinforcement Learning

Date/Time: Friday Sep 25th 11am EDT

Recording

Amir Ali Ahmadi

Princeton University

Title: Learning Dynamical Systems with Side Information

Date/Time: Friday Oct 2nd 11am EDT

Recording

Susan Murphy

Harvard University

Title: Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health

Date/Time: Friday Oct 9th 11am EDT

Recording

Ming Yuan

Columbia University

Title: Complexity of High Dimensional Sparse Functions

Date/Time: Friday Nov 13th 11am EST

Hongyu Zhao

Yale University

Title: Predicting Disease Risk from Genomics Data

Date/Time: Tuesday Nov 17th 11am EST

Recording

Peter Bartlett

UC Berkeley

Title: Benign Overfitting

Date/Time: Friday Dec 4th 3pm EDT

Recording

Yuejie Chi

Carnegie Mellon University

Title: Preconditioning Helps: Faster Convergence in Statistical and Reinforcement Learning

Date/Time: Friday Jan 15th 11am EDT

Recording

Xi Chen

New York University

Title: Robust Online Learning and its Applications to Assortment Optimization

Date/Time: Friday Jan 22th 4pm EDT

Recording

Adam Wierman

California Institute of Technology

Title: Competitive Control via Online Optimization

Date/Time: Friday Jan 29th 11am EDT

Recording

Dimitris Bertsimas

Massachusetts Institute of Technology

Title: Machine Learning under a Modern Optimization Lens

Date/Time: Friday Feb 5th 11am EST

Recording

Csaba Szepesvari

University of Alberta/DeepMind

Title: Hardness of MDP Planning with Linear Function Approximation

Date/Time: Friday Feb 12th 11am EST

Recording

Yan Liu

University of Southern California

Title: Deciphering Neural Networks through the Lens of Feature Interactions

Date/Time: Friday Feb 26th 3pm EST

Recording

Na Li

Harvard University

Title: Real-Time Distributed Decision Making in Networked Systems

Date/Time: Friday Mar 26th 11am EDT

Recording

Sebastien Bubeck

Microsoft Research

Title: A law of robustness for two-layers neural networks

Date/Time: Friday April 2nd 11am EDT

Recording

Harrison Zhou

Yale University

Title: Global Convergence of EM?

Date/Time: Friday Apr 9th 11am EDT

Recording

Vivek Farias

Massachusetts Institute of Technology

Title: Causal Inference for Panel Data with General Treatment Patterns

Date/Time: Friday Apr 16th 11am EDT

Recording

Constantine Caramanis

University of Texas, Austin

Title: MLE and EM Algorithms for Log-Concave Mixtures

Date/Time: Friday Apr 23th 11am EDT

Recording

Jorge Nocedal

Northwestern University

Title: Derivative-Free Optimization of Noisy Functions

Date/Time: Friday April 30th 11am EDT

Recording

Daniel Spielman

Yale University

Title: Balancing Covaraites in Randomized Experiments

Date/Time: Friday May 7th 11am EDT

Recording

Rebecca Willet

University of Chicago

Title: Machine Learning and Inverse Problems in Imaging

Date/Time: Friday May 14th 11am EDT

Recording

Matias Cattaneo

Princeton University

Title: On Binscatter

Date/Time: Friday May 21st 11am EDT

Recording

Stephen Wright

University of Wisconsin-Madison

Title: The role of Complexity Bounds in Optimization

Date/Time: Friday June 4th 11am EDT

Recording

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