Distributionally Robust Bayesian Nonparametric Regression
[Virtual] Hot Topics: Foundations of Stable, Generalizable and Transferable Statistical Learning March 07, 2022 - March 10, 2022
Location: SLMath: Online/Virtual
Tags/Keywords
Distributional robustness
Bayesian nonparametrics
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.
Distributionally Robust Bayesian Nonparametric Regression
Please report video problems to itsupport@slmath.org.
See more of our Streaming videos on our main VMath Videos page.