Distributionally Robust Bayesian Optimization
AISTATS, pp. 2174-2184, 2020.
Robustness to distributional shift is one of the key challenges of contemporary machine learning. Attaining such robustness is the goal of distributionally robust optimization, which seeks a solution to an optimization problem that is worst-case robust under a specified distributional shift of an uncontrolled covariate. In this paper, w...More
PPT (Upload PPT)