Research map / Probabilistic and scientific modeling / Bayesian optimization
A Unified Framework for Bayesian Optimization under Contextual Uncertainty
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Proposes a unified framework for Bayesian optimization under contextual uncertainty (BOCU), covering distributionally robust BO and expected/worst-case objectives via a first-order-optimality-based approach.
Problem
Bayesian optimization where the decision must be made before the context is observed, and the learner must manage risk under context distribution shift.
Approach
Unifies BOCU and distributionally robust BO (DRBO) as one framework, treating expected and worst-case values as special cases, and analyzes it through the first-order optimality of the risk objective.
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