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 …

The classical theory of reinforcement learning (RL) has focused on tabular and linear
representations of value functions. Further progress hinges on combining RL with modern …

Robustness to distributional shift is one of the key challenges of contemporary machine
learning. Attaining such robustness is the goal of distributionally robust optimization, which …

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

Reinforcement learning (RL) algorithms combined with modern function approximators such
as kernel functions and deep neural networks have achieved significant empirical …

Contextual bandits are a rich model for sequential decision making given side information,
with important applications, eg, in recommender systems. We propose novel algorithms for …

How should we intervene on an unknown structural equation model to maximize a
downstream variable of interest? This setting, also known as causal Bayesian optimization …

Contextual Bayesian optimization (CBO) is a powerful framework for sequential decision-
making given side information, with important applications, eg, in wind energy systems. In …

We consider model-based multi-agent reinforcement learning, where the environment
transition model is unknown and can only be learned via expensive interactions with the …

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 …