Consider the sequential optimization of an expensive to evaluate and possibly non-convex
objective function $ f $ from noisy feedback, that can be considered as a continuum-armed …

In this paper, we consider the problem of Gaussian process (GP) optimization with an added
robustness requirement: The returned point may be perturbed by an adversary, and we …

We consider the problem of optimizing a black-box function based on noisy bandit feedback.
Kernelized bandit algorithms have shown strong empirical and theoretical performance for …

Abstract Model-based reinforcement learning algorithms with probabilistic dynamical
models are amongst the most data-efficient learning methods. This is often attributed to their …

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

In the kernelized bandit problem, a learner aims to sequentially compute the optimum of a
function lying in a reproducing kernel Hilbert space given only noisy evaluations at …

In this paper, we consider the problem of black-box optimization using Gaussian Process
(GP) bandit optimization with a small number of batches. Assuming the unknown function …

We consider the problem of optimizing a high-dimensional convex function using stochastic
zeroth-order queries. Under sparsity assumptions on the gradients or function values, we …

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) …

Gaussian processes (GP) are a stochastic processes, used as Bayesian approach for the
optimization of black-box functions. Despite their effectiveness in simple problems, GP …