Playlist: 31 videos

Dynamics and Discretization: PDEs, Sampling, and Optimization

0:51:51

Optimal Transport for Learning Chaotic Dynamics via Invariant Measures

Yunan Yang (Simons Institute)
https://simons.berkeley.edu/talks/optimal-transport-for-learning-chaotic-dynamics-via-invariant-measures
Dynamics and Discretization: PDEs, Sampling, and Optimization
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0:31:10

Hessian Informed Mirror Descent With Application in Gradient Flows

Li Wang (University of Minnesota)
https://simons.berkeley.edu/talks/hessian-informed-mirror-descent-application-gradient-flows
Dynamics and Discretization: PDEs, Sampling, and Optimization
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0:55:36

Uniform Convergence Rates for Lipschitz Learning Down to Graph Connectivity

Jeff Calder (University of Minnesota)
https://simons.berkeley.edu/talks/uniform-convergence-rates
Dynamics and Discretization: PDEs, Sampling, and Optimization
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0:44:56

Optimal Transport With Stopping Times and the Stefan Problem

Inwon Kim (UCLA)
https://simons.berkeley.edu/talks/optimal-transport-stopping-times-and-stefan-problem
Dynamics and Discretization: PDEs, Sampling, and Optimization
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0:38:46

The Singular Set in the Stefan Problem

Alessio Figalli (ETH Zurich)
https://simons.berkeley.edu/talks/singular-set-stefan-problem
Dynamics and Discretization: PDEs, Sampling, and Optimization
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0:45:36

Global Well Posedness of Master Equations Mean Field Games

Wilfrid Gangbo (UCLA)
https://simons.berkeley.edu/talks/global-well-posedness-master-equations-mean-field-games
Dynamics and Discretization: PDEs, Sampling, and Optimization
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0:32:41

Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)

Jan Stanczuk (University of Cambridge)
https://simons.berkeley.edu/talks/wasserstein-gans-work-because-they-fail
Dynamics and Discretization: PDEs, Sampling, and Optimization
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0:45:5

Linearised Optimal Transport Distances

Matthew Thorpe (University of Manchester)
https://simons.berkeley.edu/talks/linearised-optimal-transport-distances
Dynamics and Discretization: PDEs, Sampling, and Optimization
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0:36:1

Archetypal Analysis

Braxton Osting (University of Utah)
https://simons.berkeley.edu/talks/archetypal-analysis
Dynamics and Discretization: PDEs, Sampling, and Optimization
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0:49:56

Mean-Field Theory of Two-Layers Neural Networks: Dimension Free Bounds and Example

Theodor Misiakiewicz (Stanford University)
https://simons.berkeley.edu/talks/mean-field-theory-two-layers-neural-networks-dimension-free-bounds-and-example
Dynamics and Discretization: PDEs, Sampling, and Optimization
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