3 accepted papers
We study Bayesian optimization problems where observation of the objective function fails stochastically, e.g., synthesis failures in materials development. For this problem, although several heuristic methods have been proposed, they do not have theoretical guarantees and sometimes deteriorate in p…
Real-world black-box optimization tasks often focus on obtaining the best reward, which includes an intrinsic random quantity from uncontrollable environmental factors. For this problem, we formulate a novel risk-seeking optimization problem whose aim is to obtain the best possible reward within a f…
Real-world optimization problems often require black-box optimization with observation failure, where we can obtain the objective function value if we succeed, otherwise, we can only obtain a fact of failure. Moreover, this failure region can be complex by several latent constraints, whose number is…