All talks are listed in Pacific Time. Schedule subject to change.

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Coffee and Check In
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Opening Remarks
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Opening Remarks
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The Restricted Gaussian Oracle as a Proximity Operator for Sampling
Adil Salim (KAUST)
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Convergence in KL Divergence of the Inexact Langevin Algorithm with Application to Score-based Generative Models
Andre Wibisono (Yale University)
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Break
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Quantization of Measures via Interacting Particle-based Optimization
Dejan Slepcev (Carnegie Mellon University)
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Lunch
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High-dimensional Scaling Limits of Least-square Online SGD Iterates and Its Fluctuations
Krishnakumar Balasubramanian (UC Davis)
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Lower Bounds for Sampling: Dimension Two and Gaussians
Sinho Chewi (Massachusetts Institute of Technology)
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Break
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Reception
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Coffee and Check In
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Feature Learning in Two-layer Neural Networks
Murat Erdogdu (University of Toronto)
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Score-based Generative Modeling: Convergence Theory
Holden Lee (Duke University)
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Break
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TBA
Augusto Gerolin (University of Ottawa)
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Lunch
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The Ensemble Kalman Filter in the Near-Gaussian Setting
Franca Hoffmann (California Institute of Technology)
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Geometric Methods in Private Convex Optimization
Kevin Tian (Microsoft Research)
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Break + Discussion
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Coffee and Check In
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Geometry Aware Exploratory Data Analysis
Paromita Dubey (Stanford)
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A Proximal Algorithm for Sampling
Yongxin Chen (Georgia Institute of Technology)
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Break
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Analysis of Adversarial Robustness and of Other Problems in Modern Machine Learning
Nicolas Garcia Trillos (University of Wisconsin Madison)
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Lunch
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The Geometry of Adversarial Training - Nonlocal Regularization and Gamma-Convergence
Leon Bungert (University of Bonn)
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TBA
Aram-Alexandre Pooladian (New York University)
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Break + Discussion