Text Classification: A Sequential Reading Approach. - Gabriel Dulac-Arnold; Ludovic Denoyer; Patrick Gallinari (ECIR, 2011)

Datum-Wise Classification: A Sequential Approach to Sparsity. - Gabriel Dulac-Arnold; Ludovic Denoyer; Philippe Preux; Patrick Gallinari (ECML/PKDD (1), 2011)

Lecture Séquentielle de Documents pour la Classification. - Gabriel Dulac-Arnold; Ludovic Denoyer; Patrick Gallinari (CORIA, 2012)

Fast Reinforcement Learning with Large Action Sets Using Error-Correcting Output Codes for MDP Factorization. - Gabriel Dulac-Arnold; Ludovic Denoyer; Philippe Preux; Patrick Gallinari (ECML/PKDD (2), 2012)

Sequential approaches for learning datum-wise sparse representations. - Gabriel Dulac-Arnold; Ludovic Denoyer; Philippe Preux; Patrick Gallinari (Mach. Learn., 2012)

Sequentially Generated Instance-Dependent Image Representations for Classification. - Gabriel Dulac-Arnold; Ludovic Denoyer; Nicolas Thome; Matthieu Cord; Patrick Gallinari (ICLR, 2014)

Reinforcement Learning in Large Discrete Action Spaces. - Gabriel Dulac-Arnold; Richard Evans 001; Peter Sunehag; Ben Coppin (CoRR, 2015)

Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions. - Peter Sunehag; Richard Evans 001; Gabriel Dulac-Arnold; Yori Zwols; Daniel Visentin; Ben Coppin (CoRR, 2015)

The Predictron: End-To-End Learning and Planning. - David Silver; Hado van Hasselt; Matteo Hessel; Tom Schaul; Arthur Guez; Tim Harley; Gabriel Dulac-Arnold; David P. Reichert; Neil C. Rabinowitz; André Barreto 001; Thomas Degris (ICML, 2017)

Learning from Demonstrations for Real World Reinforcement Learning. - Todd Hester; Matej Vecerík; Olivier Pietquin; Marc Lanctot; Tom Schaul; Bilal Piot; Andrew Sendonaris; Gabriel Dulac-Arnold; Ian Osband; John P. Agapiou; Joel Z. Leibo; Audrunas Gruslys (CoRR, 2017)

Deep Q-learning From Demonstrations. - Todd Hester; Matej Vecerík; Olivier Pietquin; Marc Lanctot; Tom Schaul; Bilal Piot; Dan Horgan; John Quan; Andrew Sendonaris; Ian Osband; Gabriel Dulac-Arnold; John P. Agapiou; et al. (AAAI, 2018)

Challenges of Real-World Reinforcement Learning. - Gabriel Dulac-Arnold; Daniel J. Mankowitz; Todd Hester (CoRR, 2019)

Deep multi-class learning from label proportions. - Gabriel Dulac-Arnold; Neil Zeghidour; Marco Cuturi; Lucas Beyer; Jean-Philippe Vert (CoRR, 2019)

Differentiable Deep Clustering with Cluster Size Constraints. - Aude Genevay; Gabriel Dulac-Arnold; Jean-Philippe Vert (CoRR, 2019)

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. - Çaglar Gülçehre; Ziyu Wang 001; Alexander Novikov 001; Thomas Paine; Sergio Gómez Colmenarejo; Konrad Zolna; Rishabh Agarwal; Josh Merel; Daniel J. Mankowitz; Cosmin Paduraru; Gabriel Dulac-Arnold; Jerry Li; et al. (NeurIPS, 2020)

Learning to run a Power Network Challenge: a Retrospective Analysis. - Antoine Marot; Benjamin Donnot; Gabriel Dulac-Arnold; Adrian Kelly; Aidan O'Sullivan; Jan Viebahn; Mariette Awad; Isabelle Guyon; Patrick Panciatici; Camilo Romero (NeurIPS (Competition and Demos), 2020)

An empirical investigation of the challenges of real-world reinforcement learning. - Gabriel Dulac-Arnold; Nir Levine; Daniel J. Mankowitz; Jerry Li; Cosmin Paduraru; Sven Gowal; Todd Hester (CoRR, 2020)

RL Unplugged: Benchmarks for Offline Reinforcement Learning. - Çaglar Gülçehre; Ziyu Wang 001; Alexander Novikov 001; Tom Le Paine; Sergio Gómez Colmenarejo; Konrad Zolna; Rishabh Agarwal; Josh Merel; Daniel J. Mankowitz; Cosmin Paduraru; Gabriel Dulac-Arnold; Jerry Li; et al. (CoRR, 2020)

A Geometric Perspective on Self-Supervised Policy Adaptation. - Cristian Bodnar; Karol Hausman; Gabriel Dulac-Arnold; Rico Jonschkowski (CoRR, 2020)

Model-Based Offline Planning. - Arthur Argenson; Gabriel Dulac-Arnold (ICLR, 2021)

Residual Reinforcement Learning from Demonstrations. - Minttu Alakuijala; Gabriel Dulac-Arnold; Julien Mairal; Jean Ponce; Cordelia Schmid (CoRR, 2021)

Learning Dynamics Models for Model Predictive Agents. - Michael Lutter; Leonard Hasenclever; Arunkumar Byravan; Gabriel Dulac-Arnold; Piotr Trochim; Nicolas Heess; Josh Merel; Yuval Tassa (CoRR, 2021)

Challenges of real-world reinforcement learning: definitions, benchmarks and analysis. - Gabriel Dulac-Arnold; Nir Levine; Daniel J. Mankowitz; Jerry Li; Cosmin Paduraru; Sven Gowal; Todd Hester (Mach. Learn., 2021)

A Metric Space Perspective on Self-Supervised Policy Adaptation. - Cristian Bodnar; Karol Hausman; Gabriel Dulac-Arnold; Rico Jonschkowski (IEEE Robotics Autom. Lett., 2021)

C3PO: Learning to Achieve Arbitrary Goals via Massively Entropic Pretraining. - Alexis Jacq; Manu Orsini; Gabriel Dulac-Arnold; Olivier Pietquin; Matthieu Geist; Olivier Bachem (CoRR, 2022)

Investigating the Role of Model-Based Learning in Exploration and Transfer. - Jacob C. Walker; Eszter Vértes; Yazhe Li; Gabriel Dulac-Arnold; Ankesh Anand; Theophane Weber; Jessica B. Hamrick (ICML, 2023)

Learning Reward Functions for Robotic Manipulation by Observing Humans. - Minttu Alakuijala; Gabriel Dulac-Arnold; Julien Mairal; Jean Ponce; Cordelia Schmid (ICRA, 2023)

Get Back Here: Robust Imitation by Return-to-Distribution Planning. - Geoffrey Cideron; Baruch Tabanpour; Sebastian Curi; Sertan Girgin; Léonard Hussenot; Gabriel Dulac-Arnold; Matthieu Geist; Olivier Pietquin; Robert Dadashi (CoRR, 2023)

Barkour: Benchmarking Animal-level Agility with Quadruped Robots. - Ken Caluwaerts; Atil Iscen; J. Chase Kew; Wenhao Yu 003; Tingnan Zhang; Daniel Freeman; Kuang-Huei Lee; Lisa Lee; Stefano Saliceti; Vincent Zhuang; Nathan Batchelor; Steven Bohez; et al. (CoRR, 2023)

RoboVQA: Multimodal Long-Horizon Reasoning for Robotics. - Pierre Sermanet; Tianli Ding; Jeffrey Zhao; Fei Xia 002; Debidatta Dwibedi; Keerthana Gopalakrishnan; Christine Chan; Gabriel Dulac-Arnold; Sharath Maddineni; Nikhil J. Joshi; Pete Florence; Wei Han 002; et al. (ICRA, 2024)

The Design of the Barkour Benchmark for Robot Agility. - Wenhao Yu 003; Ken Caluwaerts; Atil Iscen; J. Chase Kew; Tingnan Zhang; Daniel Freeman; Lisa Lee; Stefano Saliceti; Vincent Zhuang; Nathan Batchelor; Steven Bohez; Federico Casarini; et al. (IROS, 2024)