Bayesian optimization is a methodology for optimizing expensive objective functions that
has proven success in the sciences, engineering, and beyond. This timely text provides a …

Abstract Out-of-distribution (OOD) detection is indispensable for machine learning models
deployed in the open world. Recently, the use of an auxiliary outlier dataset during training …

We present the first study on provably efficient randomized exploration in cooperative multi-
agent reinforcement learning (MARL). We propose a unified algorithm framework for …

We study the efficiency of Thompson sampling for contextual bandits. Existing Thompson
sampling-based algorithms need to construct a Laplace approximation (ie, a Gaussian …

Large language models (LLMs) have shown remarkable instruction-following capabilities
and achieved impressive performances in various applications. However, the performances …

A central problem in sequential decision making is to develop algorithms that are practical
and computationally efficient, yet support the use of flexible, general-purpose models …

Thompson sampling (TS) is a popular heuristic for action selection, but it requires sampling
from a posterior distribution. Unfortunately, this can become computationally intractable in …

We study a general class of contextual bandits, where each context-action pair is associated
with a raw feature vector, but the reward generating function is unknown. We propose a …

Consider the sequential optimization of a continuous, possibly non-convex, and expensive
to evaluate objective function $ f $. The problem can be cast as a Gaussian Process (GP) …

Kernelized bandits, also known as Bayesian optimization (BO), has been a prevalent
method for optimizing complicated black-box reward functions. Various BO algorithms have …