5 papers
Pareto-Optimality, Smoothness, and Stochasticity in Learning-Augmented One-Max-Search
Ziyad Benomar, Lorenzo Croissant, Vianney Perchet +1
One-max search is a classic problem in online decision-making, in which a trader acts on a sequence of revealed prices and accepts one of them irrevocably to maximise its profit. T…
Linear Bandits beyond Inner Product Spaces, the case of Bandit Optimal Transport
Lorenzo Croissant
Linear bandits have long been a central topic in online learning, with applications ranging from recommendation systems to adaptive clinical trials. Their general learnability has…
Near-continuous time Reinforcement Learning for continuous state-action spaces
Lorenzo Croissant, Marc Abeille, Bruno Bouchard
We consider the Reinforcement Learning problem of controlling an unknown dynamical system to maximise the long-term average reward along a single trajectory. Most of the literature…
Diffusive limit approximation of pure jump optimal ergodic control problems
Marc Abeille, Bruno Bouchard, Lorenzo Croissant
Motivated by the design of fast reinforcement learning algorithms, we study the diffusive limit of a class of pure jump ergodic stochastic control problems. We show that, whenever…
Real-Time Optimisation for Online Learning in Auctions
Lorenzo Croissant, Marc Abeille, Clément Calauzènes
In display advertising, a small group of sellers and bidders face each other in up to 10 12 auctions a day. In this context, revenue maximisation via monopoly price learning is a h…