Many design tasks can be cast as black-box function optimization, enabling use of Bayesian optimization to find an ideal design with minimal number of trials. However, often we do not actually need the optimum but instead a sufficiently good solution is enough, for instance a material that is durable enough for its intended use. In most cases there are multiple satisfactory solutions, forming a superlevel set of the function, raising a key question of which one to prefer. We answer this by explaining why robustness to input perturbations that may occur when the solution is deployed is a good criterion and by introduce a Bayesian optimization method that efficiently finds satisficing solutions that are robust to maximally large perturbations. In contrast to previous works, we assume the inputs can be accurately controlled during optimization, but will be perturbed after the deployment.
Results show that surrogate-guided search can substantially reduce the evaluation cost of gradient-free post-training while producing stronger deployable single experts.
Nigel Bastian Cendra, Abdelhamid Ezzerg, Fernando Julio Cendra et al.· 0 citations
It is empirically observed that the total cost of Bayesian Optimization is U-shaped in $n_0$, and this tradeoff persists across MLE, Bayesian MCMC, and exact GP hyperparameters, as well as across acquisition functions.
Mujin Cheon, J. Odgers, Dong-Yeun Koh et al.· arXiv.org· 0 citations
This work introduces agentic Bayesian optimization: a paradigm in which an LLM agent is the central decision maker in the BO loop while a Bayesian backend provides the uncertainty-aware optimization substrate, and demonstrates the practical value of agentic BO in dynamic settings.
Paul Brunzema, Louis C. Tiao, Nhat Le et al.· 1 citation
A policy-based deep adaptive design framework is considered, which has previously been used for the EIG criterion, for adaptive design ofBayesian optimal experimental design, to address computational challenges when applying this criterion for adaptive design.
David Chen, Michael Evans, Xin-Wei Li et al.· 0 citations
This work proposes an approach to evaluating policies that provides tighter performance guarantees if the decision maker's belief happens to be correct, and shows that if computing a policy's worst-case performance is a convex program, then the value of human expertise is equal to the minimax gap of a max-min problem.
A shrinkage path heuristic is proposed that reduces the solution of a DRO problem to a one-dimensional search over the line segment connecting the sample average approximation (SAA) and the (more demanding but practically solvable) classical robust optimization solution.
Ling-Jun Meng, Ryan Cory-Wright, W. Wiesemann· 0 citations
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