Mathematical Sciences Research Institute

Home » Workshop » Schedules » Distributionally Robust Bayesian Nonparametric Regression

Distributionally Robust Bayesian Nonparametric Regression

[Virtual] Hot Topics: Foundations of Stable, Generalizable and Transferable Statistical Learning March 07, 2022 - March 10, 2022

March 09, 2022 (09:00 AM PST - 09:25 AM PST)
Speaker(s): Jose Blanchet (Stanford University)
Location: MSRI: Online/Virtual
  • Distributional robustness

  • Bayesian nonparametrics

Primary Mathematics Subject Classification
Secondary Mathematics Subject Classification

Distributionally Robust Bayesian Nonparametric Regression


A distributionally robust Bayesian nonparametric regression estimator is the solution of a min-max game in which the statistician chooses a regression function of observations (i.e. an element in L2) and the adversary, knowing the statistician's selection, maximizes the mean-squared error incurred over a Wasserstein-type-2 ball around a full nonparametric Bayesian model, which we assume to be Gaussian on a suitable Hilbert space. We study this doubly infinite-dimensional game, show the existence of a Nash equilibrium and its evaluation.

Supplements No Notes/Supplements Uploaded
Video/Audio Files

Distributionally Robust Bayesian Nonparametric Regression

Troubles with video?

Please report video problems to itsupport@msri.org.

See more of our Streaming videos on our main VMath Videos page.