Add to ORKGInternational audienceWe show complexity bounds for noisy optimization, in frame- works in which noise is stronger than in previously published papers[19]. We also propose an algorithm based on bandits (variants of [16]) that reaches the bound within logarithmic factors. We emphasize the differ- ences with empirical derived published algorithms
In this paper, we study the stochastic bandits problem with k unknown heavy-tailed and corrupted rew...
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...
International audienceWe show complexity bounds for noisy optimization, in frame- works in which noi...
International audienceThis paper exhibits lower and upper bounds on runtimes for expensive noisy opt...
International audienceThis paper considers the problem of maximizing an expectation function over a ...
We consider the problem of optimizing an unknown (typically non-convex) function with a bounded norm...
International audienceWe consider in this work the application of optimization algorithms to problem...
This manuscript concentrates in studying methods to handle the noise, including using resampling met...
Many applications require optimizing an un-known, noisy function that is expensive to evaluate. We f...
For bandit convex optimization we propose a model, where a gradient estimation oracle acts as an int...
International audienceIn spite of various recent publications on the subject, there are still gaps b...
In this paper, we study the stochastic bandits problem with k unknown heavy-tailed and corrupted rew...
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...
International audienceWe show complexity bounds for noisy optimization, in frame- works in which noi...
International audienceThis paper exhibits lower and upper bounds on runtimes for expensive noisy opt...
International audienceThis paper considers the problem of maximizing an expectation function over a ...
We consider the problem of optimizing an unknown (typically non-convex) function with a bounded norm...
International audienceWe consider in this work the application of optimization algorithms to problem...
This manuscript concentrates in studying methods to handle the noise, including using resampling met...
Many applications require optimizing an un-known, noisy function that is expensive to evaluate. We f...
For bandit convex optimization we propose a model, where a gradient estimation oracle acts as an int...
International audienceIn spite of various recent publications on the subject, there are still gaps b...
In this paper, we study the stochastic bandits problem with k unknown heavy-tailed and corrupted rew...
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...