Bayesian Optimization for Function-Valued Responses under Min-Max Criteria

ArXi:2512.07868v2 Announce Type: replace Bayesian optimization is widely used for optimizing expensive black box functions, but most existing approaches focus on scalar responses. In many scientific and engineering settings the response is functional, varying smoothly over an index such as time or wavelength, which makes classical formulations inadequate. Existing methods often minimize integrated error, which captures average performance but neglects worst case deviations.