Bayesian optimization (BO) is a prominent approach to optimizing expensive-to-evaluate
black-box functions. The massive computational capability of edge devices such as mobile …

Bayesian optimization (BO) has recently been extended to the federated learning (FL)
setting by the federated Thompson sampling (FTS) algorithm, which has promising …

Bayesian optimization (BO), which uses a Gaussian process (GP) as a surrogate to model its
objective function, is popular for black-box optimization. However, due to the limitations of …

In distributionally robust Bayesian optimization (DRBO), an exact computation of the worst-
case expected value requires solving an expensive convex optimization problem. We …

Existing neural active learning algorithms have aimed to optimize the predictive
performance of neural networks (NNs) by selecting data for labelling. However, other than a …

Bayesian optimization (BO) is a widely-used sequential method for zeroth-order optimization
of complex and expensive-to-compute black-box functions. The existing BO methods …

Bayesian optimization (BO) is a popular tool for optimizing complex and costly-to-evaluate
black-box objective functions. To further reduce the number of function evaluations, any …

The problem of inverse reinforcement learning (IRL) is relevant to a variety of tasks including
value alignment and robot learning from demonstration. Despite significant algorithmic …

This paper presents a recursive reasoning formalism of Bayesian optimization (BO) to model
the reasoning process in the interactions between boundedly rational, self-interested agents …

This paper presents the private-outsourced-Gaussian process-upper confidence bound (PO-
GP-UCB) algorithm, which is the first algorithm for privacy-preserving Bayesian optimization …