Add to ORKGKernel-based bandit is an extensively studied black-box optimization problem, in which the objective function is assumed to live in a known reproducing kernel Hilbert space. While nearly optimal regret bounds (up to logarithmic factors) are established in the noisy setting, surprisingly, less is known about the noise-free setting (when the exact values of the underlying function is accessible without observation noise). We discuss several upper bounds on regret; none of which seem order optimal, and provide a conjecture on the order optimal regret bound.Comment: Conference on Learning Theory (COLT) 202
© 2018 Curran Associates Inc.All rights reserved. Bayesian optimization usually assumes that a Bayes...
We study algorithms using randomized value functions for exploration in reinforcement learning. This...
International audienceWe derive an alternative proof for the regret of Thompson sampling (\ts) in th...
Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We fo...
Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We fo...
We consider the problem of optimizing a black-box function based on noisy bandit feedback. Kernelize...
We consider the problem of optimising functions in the reproducing kernel Hilbert space (RKHS) of a ...
International audienceBandit algorithms are concerned with trading exploration with exploitation whe...
International audienceGaussian processes (GP) are a stochastic processes, used as Bayesian approach ...
Many applications require optimizing an un-known, noisy function that is expensive to evaluate. We f...
Many applications in machine learning require optimizing unknown functions defined over a high-dimen...
This thesis presents some statistical refinements of the bandits approach presented in [11] in the s...
We consider the problem of optimizing an unknown (typically non-convex) function with a bounded norm...
Bandit algorithms are concerned with trading exploration with exploitation where a number of options...
This paper analyzes the problem of Gaussian process (GP) bandits with deterministic observations. Th...
© 2018 Curran Associates Inc.All rights reserved. Bayesian optimization usually assumes that a Bayes...
We study algorithms using randomized value functions for exploration in reinforcement learning. This...
International audienceWe derive an alternative proof for the regret of Thompson sampling (\ts) in th...
Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We fo...
Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We fo...
We consider the problem of optimizing a black-box function based on noisy bandit feedback. Kernelize...
We consider the problem of optimising functions in the reproducing kernel Hilbert space (RKHS) of a ...
International audienceBandit algorithms are concerned with trading exploration with exploitation whe...
International audienceGaussian processes (GP) are a stochastic processes, used as Bayesian approach ...
Many applications require optimizing an un-known, noisy function that is expensive to evaluate. We f...
Many applications in machine learning require optimizing unknown functions defined over a high-dimen...
This thesis presents some statistical refinements of the bandits approach presented in [11] in the s...
We consider the problem of optimizing an unknown (typically non-convex) function with a bounded norm...
Bandit algorithms are concerned with trading exploration with exploitation where a number of options...
This paper analyzes the problem of Gaussian process (GP) bandits with deterministic observations. Th...
© 2018 Curran Associates Inc.All rights reserved. Bayesian optimization usually assumes that a Bayes...
We study algorithms using randomized value functions for exploration in reinforcement learning. This...
International audienceWe derive an alternative proof for the regret of Thompson sampling (\ts) in th...