All talks are listed in Pacific Time. Please note some speakers may present remotely. Schedule subject to change.

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Coffee & Check In
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Opening Remarks
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How Many Clusters - An Algorithmic Answer

Ravi Kannan (Microsoft Research India)
Video
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Break
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The Mirror Langevin Algorithm Converges with Vanishing Bias

Andre Wibisono (Yale)
Video
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Break
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Faster Polytope Rounding, Sampling, and Volume Computation via a Sub-Linear Ball Walk

Oren Mangoubi (WPI)
Video
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Lunch
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Improved Dimension Dependence for the Metropolis-Adjusted Langevin Algorithm and Lower Bounds for Sampling

Sinho Chewi (MIT)
Video
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Break
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Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions

Kevin Tian (Stanford)
Video
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Reception
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Coffee & Check In
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On Langevin Monte Carlo for Log-Concave Densities

Arnak Dalalyan (ENSAE Paris)
Video
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Break
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Non-Equilibrium Sampling

Alain Durmus (ENS Paris-Saclay)
Video
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Break
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Primal--Dual Optimization and Application to Sampling

Adil Salim (KAUST)
Video
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Lunch
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Structured Logconcave Sampling with a Restricted Gaussian Oracle

Ruoqi Shen (University of Washington)
Video
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Break
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Algorithms for Sampling Convex Bodies

Aditi Laddha (Georgia Institute of Technology)
Video
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Break
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Random Coordinate Descent and Langevin Monte Carlo

Qin Li (UW Madison)
Video
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Coffee & Check In
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A Geometric and Analytical Perspective on Adversarial Learning Problems

Nicolas Garcia Trillos (University of Wisconsin-Madison)
Video
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Break
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Current and Future Applications of Sampling Algorithms in Modelling Biochemical Networks

Ronan Fleming (Leiden University)
Video
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Break
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A Blob Method for Diffusion and Applications to Sampling and Two Layer Neural Networks

Katy Craig (UC Santa Barbara)
Video
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Lunch
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A Mean-Field Theory of Lazy Training in Two-Layer Neural Nets: Entropic Regularization and Controlled McKean-Vlasov Dynamics

Belinda Tzen (UIUC)
Video
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Break
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Kernel Stein Discrepancy Descent

Anna Korba (ENSAE Paris)
Video
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Break
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Open Problems Session
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Coffee & Check In
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Sampling Convex Bodies: A Status Report

Santosh Vempala (Georgia Tech)
Video
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Break
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Quantitative Convergence Analysis of Hypocoercive Sampling Dynamics

Jianfeng Lu (Duke University)
Video
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Break
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Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave Sampling

Yuansi Chen (Duke University)
Video
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Lunch
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High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm

Wenlong Mou (UC Berkeley)
Video
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Break
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When Is the Convergence Time of Langevin Algorithms Dimension Independent: A Composite Optimization Viewpoint

Yian Ma (UC San Diego)
Video
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Break
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Generative Models for Sampling on Supports Inspired by Observed Data

Susan Holmes (Stanford)
Video
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Coffee & Check In
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MCMC Sampling Around Redistricting and Gerrymandering

Jonathan Mattingly (Duke University)
Video
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Break
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Sliced Normalizing Flow Optimization and Monte Carlo

Uros Seljak (UC Berkeley)
Video
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Break
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Adaptive Wavelet Distillation from Neural Networks through Interpretation

Bin Yu (UC Berkeley)
Video
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Lunch
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Quantitative Faber-Krahn Inequalities and the ACF Monotonicity Formula

Robin Neumayer (Northwestern University)
Video
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Break
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On the Convergence of Monte Carlo Methods with Stochastic Gradients

Quanquan Gu (UCLA)
Video
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Break
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Optimal Gradient-based Algorithms for Non-concave Bandit Optimization

Qi Lei (Princeton)
Video