News & Events

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.

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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

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FODSI Seminar

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

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FODSI Seminar

Alex Andoni (Columbia). Approximating Edit Distance in Near-Linear Time.

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FODSI Seminar

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

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FODSI Seminar

Francis Bach (INRIA). Distributed Machine Learning over Networks.

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NSF announcement

NSF Announces $12.5M Award

The 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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