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)
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Break
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The Mirror Langevin Algorithm Converges with Vanishing Bias
Andre Wibisono (Yale)
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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)
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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)
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Break
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Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions
Kevin Tian (Stanford)
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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)
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Break
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Non-Equilibrium Sampling
Alain Durmus (ENS Paris-Saclay)
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Break
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Primal--Dual Optimization and Application to Sampling
Adil Salim (KAUST)
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Lunch
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Structured Logconcave Sampling with a Restricted Gaussian Oracle
Ruoqi Shen (University of Washington)
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Break
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Algorithms for Sampling Convex Bodies
Aditi Laddha (Georgia Institute of Technology)
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Break
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Random Coordinate Descent and Langevin Monte Carlo
Qin Li (UW Madison)
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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)
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Break
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Current and Future Applications of Sampling Algorithms in Modelling Biochemical Networks
Ronan Fleming (Leiden University)
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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)
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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)
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Break
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Kernel Stein Discrepancy Descent
Anna Korba (ENSAE Paris)
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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)
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Break
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Quantitative Convergence Analysis of Hypocoercive Sampling Dynamics
Jianfeng Lu (Duke University)
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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)
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Lunch
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High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm
Wenlong Mou (UC Berkeley)
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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)
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Break
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Generative Models for Sampling on Supports Inspired by Observed Data
Susan Holmes (Stanford)
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Coffee & Check In
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MCMC Sampling Around Redistricting and Gerrymandering
Jonathan Mattingly (Duke University)
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Break
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Sliced Normalizing Flow Optimization and Monte Carlo
Uros Seljak (UC Berkeley)
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Break
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Adaptive Wavelet Distillation from Neural Networks through Interpretation
Bin Yu (UC Berkeley)
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Lunch
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Quantitative Faber-Krahn Inequalities and the ACF Monotonicity Formula
Robin Neumayer (Northwestern University)
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Break
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On the Convergence of Monte Carlo Methods with Stochastic Gradients
Quanquan Gu (UCLA)
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Break
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Optimal Gradient-based Algorithms for Non-concave Bandit Optimization
Qi Lei (Princeton)