Santosh Vempala

0:49:30

Santosh Vempala: The KLS conjecture I

0:54:28

Reducing Isotropy to KLS: An Almost Cubic Volume Algorithm by Santosh Vempala

0:29:52

Santosh S Vempala, The Communication Complexity of Optimization

0:25:40

Santosh Vempala - Gibbs Sampling for Convex Bodies and an L_0 Isoperimetric Inequality

1:01:40

Panel Discussion: Open Problems in the Theory of Deep Learning

0:32:48

Santosh Vempala -- Multicriteria Dimensionality Reduction

1:31:08

Unsupervised Learning

0:52:48

Santosh Vempala: The KLS conjecture II

1:08:30

TCS+ talk: Santosh Vempala

0:59:28

Robustly Learning Mixtures of Arbitrary Gaussians in Polynomial Time by Santosh Vempala

1:12:50

Lifelong Learning of Representations with Provable Guarantees by Santosh Vempala (Georgia Tech)

0:43:59

Santosh Vempala (Gatech) -- Is There a Tractable (and Interesting) Theory of Nonconvex Optimization?

0:34:04

Fast Algorithms for Multivariate ODEs arising in Sampling and Learning

0:05:17

Georgia Tech professor discusses how generative AI works

0:42:36

Sampling Convex Bodies: A Status Report

0:32:51

Continuous Algorithms: Sampling and Optimization in High Dimension

0:08:44

Cortical Computation via Iterative Constructions

0:54:39

An SDP-based Algorithmic Cheeger Inequality for Vertex Expansion

0:49:25

Santosh Vempala: Beyond Moments: Robust certificates for affine transformations

0:58:31

On The Complexity of Training a Neural Network by Santhosh Vempala

0:57:17

High Dimensional Geometry and Concentration I

0:57:41

Subsampled Power Iteration: A Unified Algorithm for Block Models and Planted CSP's

1:02:53

High Dimensional Geometry and Concentration II

0:30:41

The Convergence of Hamiltonian Monte Carlo