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 …

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) …

In this paper, we consider algorithm independent lower bounds for the problem of black-box
optimization of functions having a bounded norm is some Reproducing Kernel Hilbert Space …

Confidence intervals are a crucial building block in the analysis of various online learning
problems. The analysis of kernel-based bandit and reinforcement learning problems utilize …

Consider the problem of optimizing a black-box function under the assumption that the
function is Holder smooth and has bounded norm in the reproducing kernel Hilbert space …

We consider the problem of sequentially maximizing an unknown function $ f $ over a set of
actions of the form $(s, x) $, where the selected actions must satisfy a safety constraint with …

Gaussian process optimization is a successful class of algorithms (eg GP-UCB) to optimize a
black-box function through sequential evaluations. However, for functions with continuous …

In continuum-armed bandit problems where the underlying function resides in a reproducing
kernel Hilbert space (RKHS), namely, the kernelised bandit problems, an important open …

In this paper, we study the problem of Gaussian process (GP) bandits under relaxed
optimization criteria stating that any function value above a certain threshold is “good …

We study the problem of designing an adaptive strategy for querying a noisy zeroth-order-
oracle to efficiently learn about the optimizer of an unknown function $ f $. To make the …