Videos

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Supervised learning with missing values Thumbnail

Supervised learning with missing values

Presenter
  • Julie Josse
September 8, 2020
IAS
Periodic equilibria for a first order Mean Field Game Thumbnail

Periodic equilibria for a first order Mean Field Game

Presenter
  • Annalisa Cesaroni
May 7, 2020
IPAM
A General Theory for Discrete-Time Mean-Field Games Thumbnail

A General Theory for Discrete-Time Mean-Field Games

Presenter
  • Tamer Basar
May 6, 2020
IPAM
Deterministics Mean Field Games with control on the acceleration Thumbnail

Deterministics Mean Field Games with control on the acceleration

Presenter
  • Paola Mannucci
May 4, 2020
IPAM
The joint power of deterministic and Bayesian approaches for seismic inverse problems Thumbnail

The joint power of deterministic and Bayesian approaches for seismic inverse problems

Presenter
  • Yunan Yang
April 22, 2020
IPAM
Rank optimality of the Burer-Monteiro factorization Thumbnail

Rank optimality of the Burer-Monteiro factorization

Presenter
  • Irene Waldspurger
April 22, 2020
IPAM
Active learning and experimental design - who should we test? Thumbnail

Active learning and experimental design - who should we test?

Presenter
  • Eldad Haber
April 21, 2020
IPAM
A Lyapunov analysis for accelerated gradient methods: From deterministic to stochastic case Thumbnail

A Lyapunov analysis for accelerated gradient methods: From deterministic to stochastic case

Presenter
  • Maxime Laborde
April 20, 2020
IPAM
Probabilistic max-plus schemes for solving Hamilton-Jacobi-Bellman equations Thumbnail

Probabilistic max-plus schemes for solving Hamilton-Jacobi-Bellman equations

Presenter
  • Marianne Akian
April 3, 2020
IPAM
Al'brekht’s Method in Infinite Dimensions Thumbnail

Al'brekht’s Method in Infinite Dimensions

Presenter
  • Arthur Krener
March 30, 2020
IPAM
Solving Game-Theoretic Hamilton-Jacobi Equations in a Model-Free Way Thumbnail

Solving Game-Theoretic Hamilton-Jacobi Equations in a Model-Free Way

Presenter
  • Kyriakos Vamvoudakis
March 30, 2020
IPAM
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning Thumbnail

Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning

Presenter
  • Daniel Kuhn
February 26, 2020
IPAM