6 citations · 6 across the 2 of their papers we have counts for
8 papers
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation
Victor Picheny, Vincent Dutordoir, Artem Artemev +1
Many machine learning models require a training procedure based on running stochastic gradient descent. A key element for the efficiency of those algorithms is the choice of the le…
Ordinal Bayesian Optimisation
Victor Picheny, Sattar Vakili, Artem Artemev
Bayesian optimisation is a powerful tool to solve expensive black-box problems, but fails when the stationary assumption made on the objective function is strongly violated, which…
X-Armed Bandits: Optimizing Quantiles, CVaR and Other Risks
Léonard Torossian, Aurélien Garivier, Victor Picheny
We propose and analyze StoROO, an algorithm for risk optimization on stochastic black-box functions derived from StoOO. Motivated by risk-averse decision making fields like agricul…
The Kalai-Smorodinski solution for many-objective Bayesian optimization
Mickaël Binois, Victor Picheny, Patrick Taillandier +1
An ongoing aim of research in multiobjective Bayesian optimization is to extend its applicability to a large number of objectives. While coping with a limited budget of evaluations…
A Review on Quantile Regression for Stochastic Computer Experiments
Léonard Torossian, Victor Picheny, Robert Faivre +1
We report on an empirical study of the main strategies for quantile regression in the context of stochastic computer experiments. To ensure adequate diversity, six metamodels are p…
Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Batch Versions
David Gaudrie, Rodolphe Le Riche, Victor Picheny +2
Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functio…