56 citations · 200 across the 32 of their papers we have counts for
19 papers · 1 filter
Foundations of Reinforcement Learning and Control:Connections and New Perspectives
Claire Vernade, Onno Eberhard, Martha White +4
Reinforcement learning and control theory are two adjacent scientific fields that focus on optimizing the controller of unknown dynamical systems using feedback. While both fields…
Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation
Ziyad Sheebaelhamd, Luca Viano, Volkan Cevher +1
This work investigates multi-objective imitation learning: the problem of recovering policies that lie on the Pareto front given demonstrations from multiple Pareto-optimal experts…
Tight Sample Complexity Bounds for Entropic Best Policy Identification
Amer Essakine, Claire Vernade
We study best-policy identification for finite-horizon risk-sensitive reinforcement learning under the entropic risk measure. Recent work established a constant gap in the exponent…
Commit to the Bit: Reactive Reinforcement Learning Done Right
Onno Eberhard, Claire Vernade, Michael Muehlebach
Reinforcement learning algorithms are commonly analyzed (and designed) under the Markov assumption. This is unrealistic, as most environments encountered in practice are either par…
Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning
Amir Rezaei Balef, Claire Vernade, Katharina Eggensperger
The Combined Algorithm Selection and Hyperparameter optimization (CASH) is a challenging resource allocation problem in the field of AutoML. We propose MaxUCB, a max k-armed bandit…
Clustered KL-barycenter design for policy evaluation
Simon Weissmann, Till Freihaut, Claire Vernade +2
In the context of stochastic bandit models, this article examines how to design sample-efficient behavior policies for the importance sampling evaluation of multiple target policie…
Partially Observable Reinforcement Learning with Memory Traces
Onno Eberhard, Michael Muehlebach, Claire Vernade
Partially observable environments present a considerable computational challenge in reinforcement learning due to the need to consider long histories. Learning with a finite window…
Quantization-Free Autoregressive Action Transformer
Ziyad Sheebaelhamd, Michael Tschannen, Michael Muehlebach +1
Current transformer-based imitation learning approaches introduce discrete action representations and train an autoregressive transformer decoder on the resulting latent code. Howe…
Online Decision Deferral under Budget Constraints
Mirabel Reid, Tom Sühr, Claire Vernade +1
Machine Learning (ML) models are increasingly used to support or substitute decision making. In applications where skilled experts are a limited resource, it is crucial to reduce t…
A Pontryagin Perspective on Reinforcement Learning
Onno Eberhard, Claire Vernade, Michael Muehlebach
Reinforcement learning has traditionally focused on learning state-dependent policies to solve optimal control problems in a closed-loop fashion. In this work, we introduce the par…
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms
MohammadJavad Azizi, Thang Duong, Yasin Abbasi-Yadkori +3
We study a sequential decision problem where the learner faces a sequence of -armed bandit tasks. The task boundaries might be known (the bandit meta-learning setting), or unkno…
EigenGame: PCA as a Nash Equilibrium
Ian Gemp, Brian McWilliams, Claire Vernade +1
We present a novel view on principal component analysis (PCA) as a competitive game in which each approximate eigenvector is controlled by a player whose goal is to maximize their…
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting
Ilja Kuzborskij, Claire Vernade, András György +1
We consider off-policy evaluation in the contextual bandit setting for the purpose of obtaining a robust off-policy selection strategy, where the selection strategy is evaluated ba…
Weighted Linear Bandits for Non-Stationary Environments
Yoan Russac, Claire Vernade, Olivier Cappé
We consider a stochastic linear bandit model in which the available actions correspond to arbitrary context vectors whose associated rewards follow a non-stationary linear regressi…
Stochastic Bandit Models for Delayed Conversions
Claire Vernade, Olivier Cappé, Vianney Perchet
Online advertising and product recommendation are important domains of applications for multi-armed bandit methods. In these fields, the reward that is immediately available is mos…
Sparse Stochastic Bandits
Joon Kwon, Vianney Perchet, Claire Vernade
In the classical multi-armed bandit problem, d arms are available to the decision maker who pulls them sequentially in order to maximize his cumulative reward. Guarantees can be ob…
Bernoulli Rank- Bandits for Click Feedback
Sumeet Katariya, Branislav Kveton, Csaba Szepesvári +2
The probability that a user will click a search result depends both on its relevance and its position on the results page. The position based model explains this behavior by ascrib…
Stochastic Rank-1 Bandits
Sumeet Katariya, Branislav Kveton, Csaba Szepesvari +2
We propose stochastic rank- bandits, a class of online learning problems where at each step a learning agent chooses a pair of row and column arms, and receives the product of t…
Multiple-Play Bandits in the Position-Based Model
Paul Lagrée, Claire Vernade, Olivier Cappé
Sequentially learning to place items in multi-position displays or lists is a task that can be cast into the multiple-play semi-bandit setting. However, a major concern in this con…