Joint IFML/CCSI Symposium

Program

Computational Complexity of Statistical Inference

Date

Tuesday, Nov. 2 – Friday, Nov. 5, 2021

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9:50 – 10 a.m.

Welcome

10 – 10:40 a.m.

A Data-Centric View on Robustness

Ludwig Schmidt (University of Washington)

10:40 – 11 a.m.

Break

11 – 11:40 a.m.

On the Power of Differentiable Learning

Nathan Srebro (Toyota Technological Institute at Chicago)

11:40 a.m. – 1:30 p.m.

Lunch

1:30 – 3 p.m.

Student Poster Session & Reception

10 – 10:40 a.m.

Yes, Generative Models Are the New Sparsity

Alex Dimakis (UT Austin)

10:40 – 11 a.m.

Break

11 – 11:40 a.m.

The Surprising Power of the Lenstra-Lenstra-Lovasz Algorithm for Noiseless Inference

Ilias Zadik (MIT)

10 – 10:40 a.m.

Learning and Optimization with Adaptive Smoothed Adversaries

Nika Haghtalab (UC Berkeley)

10:40 – 11 a.m.

Break

11 – 11:40 a.m.

Scalable and Reliable Inference for Probabilistic Modeling

Ruqi Zhang (UT Austin)

10 – 10:40 a.m.

The Planted Matching Problem: Sharp Threshold and Infinite-Order Phase Transition

Dana Yang (Simons Institute)

10:40 – 11 a.m.

Break

11 – 11:40 a.m.

Function Space View of Bounded Weight Norm Networks

Suriya Gunasekar (Microsoft Research)