Papers
1–3 of 3Research Paper·Mar 9, 2026
Local Constrained Bayesian Optimization
Bayesian optimization (BO) for high-dimensional constrained problems remains a significant challenge due to the curse of dimensionality. We propose Local Constrained Bayesian Optimization (LCBO), a no...
7.0 viability
Research Paper·Mar 12, 2026
Wasserstein Gradient Flows for Batch Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) provides a powerful, decision-theoretic framework for selecting experiments so as to maximise the expected utility of the data to be collected. In practice,...
4.0 viability
Research Paper·Mar 10, 2026
On Regret Bounds of Thompson Sampling for Bayesian Optimization
We study a widely used Bayesian optimization method, Gaussian process Thompson sampling (GP-TS), under the assumption that the objective function is a sample path from a GP. Compared with the GP upper...
2.0 viability