Distributionally Robust Bayesian Optimization

AISTATS, pp. 2174-2184, 2020.

Cited by: 5|Bibtex|Views37
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Abstract:

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

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