Caroline Uhler (MIT). Autoencoders and Causality in the Light of Drug Repurposing for COVID-19.

## FODSI Seminar

## FODSI Seminar

Cong Ma (UC Berkeley). Minimax Off-Policy Evaluation for Multi-Armed Bandits.

## FODSI Seminar

Suvrit Sra (MIT). SGD without replacement: optimal rate analysis and more.

## FODSI Seminar

Jiantao Jiao (UC Berkeley). Sharp Minimax Rates for Imitation Learning

## FODSI Seminar

Costis Daskalakis (MIT). Equilibrium Computation and the Foundations of Deep Learning

## FODSI Seminar

Peng Ding (UC Berkeley). Multiply robust estimation of causal effects under principal ignorability

## FODSI Seminar

Michael Jordan (UC Berkeley). Towards a Blend of Machine Learning and Economics

## FODSI Seminar

Rong Ge (Duke). A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Net.

Watch video## FODSI Seminar

Arnaud Doucet (Oxford). Perfect Simulation for Feynman-Kac Models using Ensemble Rejection Sampling.

## FODSI Seminar

Mary Wooters (Stanford). Sharp Thresholds for Random Subspaces, and Applications.

## FODSI Kickoff Meeting

The FODSI Kickoff meeting took place on on Nov 10, 2020 (virtually). Watch the videos on the Simons Institute Youtube Channel

Watch videos## FODSI Seminar

Carola-Bibiane Schönlieb (Cambridge). Data driven variational models for solving inverse problems.

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Gábor Lugosi (Pompeu Fabra University). On Estimating the Mean of a Random Vector.

Watch videoThe National Science Foundation has awarded $12.5 million to establish a multidisciplinary institute---a collaboration between UC Berkeley and MIT, partnering with Boston, Northeastern, Harvard and Howard Universities, and Bryn Mawr College---to improve our understanding of critical issues in data science, including modeling, statistical inference, computational efficiency, and societal impacts.

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