11 citations · 17 across the 9 of their papers we have counts for
5 papers · 1 filter
Bayesian Optimization with Structured Measurements: A Vector-Valued RKHS Framework
Wenbin Wang, Colin N. Jones
Bayesian optimization (BO) is an efficient framework for optimizing expensive black-box functions. However, it is typically formulated as learning an end-to-end mapping from inputs…
Learning efficient representations of complex constraints for scalable optimization
Yilin Wen, Yi Guo, Bo Zhao +4
Complex constraints often make real-world optimization computationally prohibitive at the scale and speed required for operational decision-making. Here we introduce PolyFormer, a…
Principled Bayesian Optimisation in Collaboration with Human Experts
Wenjie Xu, Masaki Adachi, Colin N. Jones +1
Bayesian optimisation for real-world problems is often performed interactively with human experts, and integrating their domain knowledge is key to accelerate the optimisation proc…
Principled Preferential Bayesian Optimization
Wenjie Xu, Wenbin Wang, Yuning Jiang +2
We study the problem of preferential Bayesian optimization (BO), where we aim to optimize a black-box function with only preference feedback over a pair of candidate solutions. Ins…
Multi-Agent Bayesian Optimization with Coupled Black-Box and Affine Constraints
Wenjie Xu, Yuning Jiang, Bratislav Svetozarevic +1
This paper studies the problem of distributed multi-agent Bayesian optimization with both coupled black-box constraints and known affine constraints. A primal-dual distributed algo…