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ORCIDGabriel Dulac-Arnold
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2020 – today
- 2024
[c13]Pierre Sermanet, Tianli Ding, Jeffrey Zhao, Fei Xia, Debidatta Dwibedi, Keerthana Gopalakrishnan, Christine Chan, Gabriel Dulac-Arnold, Sharath Maddineni, Nikhil J. Joshi, Pete Florence, Wei Han, Robert Baruch, Yao Lu, Suvir Mirchandani, Peng Xu, Pannag Sanketi, Karol Hausman, Izhak Shafran, Brian Ichter, Yuan Cao:
RoboVQA: Multimodal Long-Horizon Reasoning for Robotics. ICRA 2024: 645-652- 2023
- [c12]Jacob C. Walker, Eszter Vértes, Yazhe Li, Gabriel Dulac-Arnold, Ankesh Anand, Theophane Weber, Jessica B. Hamrick:
Investigating the Role of Model-Based Learning in Exploration and Transfer. ICML 2023: 35368-35383 - [c11]Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal, Jean Ponce, Cordelia Schmid:
Learning Reward Functions for Robotic Manipulation by Observing Humans. ICRA 2023: 5006-5012 - [i23]Jacob C. Walker, Eszter Vértes, Yazhe Li, Gabriel Dulac-Arnold, Ankesh Anand, Théophane Weber, Jessica B. Hamrick:
Investigating the role of model-based learning in exploration and transfer. CoRR abs/2302.04009 (2023) - [i22]Geoffrey Cideron, Baruch Tabanpour, Sebastian Curi, Sertan Girgin, Léonard Hussenot, Gabriel Dulac-Arnold, Matthieu Geist, Olivier Pietquin, Robert Dadashi:
Get Back Here: Robust Imitation by Return-to-Distribution Planning. CoRR abs/2305.01400 (2023) - [i21]Ken Caluwaerts, Atil Iscen, J. Chase Kew, Wenhao Yu, Tingnan Zhang, Daniel Freeman, Kuang-Huei Lee, Lisa Lee, Stefano Saliceti, Vincent Zhuang, Nathan Batchelor, Steven Bohez, Federico Casarini, José Enrique Chen, Omar Cortes, Erwin Coumans, Adil Dostmohamed, Gabriel Dulac-Arnold, Alejandro Escontrela, Erik Frey, Roland Hafner, Deepali Jain, Bauyrjan Jyenis, Yuheng Kuang, Tsang-Wei Edward Lee, Linda Luu, Ofir Nachum, Ken Oslund, Jason Powell, Diego Reyes, Francesco Romano, Fereshteh Sadeghi, Ron Sloat, Baruch Tabanpour, Daniel Zheng, Michael Neunert, Raia Hadsell, Nicolas Heess, Francesco Nori, Jeff Seto, Carolina Parada, Vikas Sindhwani, Vincent Vanhoucke, Jie Tan:
Barkour: Benchmarking Animal-level Agility with Quadruped Robots. CoRR abs/2305.14654 (2023) - [i20]Pierre Sermanet, Tianli Ding, Jeffrey Zhao, Fei Xia, Debidatta Dwibedi, Keerthana Gopalakrishnan, Christine Chan, Gabriel Dulac-Arnold, Sharath Maddineni, Nikhil J. Joshi, Pete Florence, Wei Han, Robert Baruch, Yao Lu, Suvir Mirchandani, Peng Xu, Pannag Sanketi, Karol Hausman, Izhak Shafran, Brian Ichter, Yuan Cao:
RoboVQA: Multimodal Long-Horizon Reasoning for Robotics. CoRR abs/2311.00899 (2023) - 2022
- [i19]Alexis Jacq, Manu Orsini, Gabriel Dulac-Arnold, Olivier Pietquin, Matthieu Geist, Olivier Bachem:
C3PO: Learning to Achieve Arbitrary Goals via Massively Entropic Pretraining. CoRR abs/2211.03521 (2022) - [i18]Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal, Jean Ponce, Cordelia Schmid:
Learning Reward Functions for Robotic Manipulation by Observing Humans. CoRR abs/2211.09019 (2022) - 2021
- [j3]Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, Todd Hester:
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis. Mach. Learn. 110(9): 2419-2468 (2021) - [j2]Cristian Bodnar
, Karol Hausman, Gabriel Dulac-Arnold, Rico Jonschkowski:
A Metric Space Perspective on Self-Supervised Policy Adaptation. IEEE Robotics Autom. Lett. 6(3): 4329-4336 (2021) - [c10]Arthur Argenson, Gabriel Dulac-Arnold:
Model-Based Offline Planning. ICLR 2021 - [i17]Antoine Marot, Benjamin Donnot, Gabriel Dulac-Arnold, Adrian Kelly, Aïdan O'Sullivan, Jan Viebahn, Mariette Awad, Isabelle Guyon, Patrick Panciatici, Camilo Romero:
Learning to run a Power Network Challenge: a Retrospective Analysis. CoRR abs/2103.03104 (2021) - [i16]Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal, Jean Ponce, Cordelia Schmid:
Residual Reinforcement Learning from Demonstrations. CoRR abs/2106.08050 (2021) - [i15]Michael Lutter, Leonard Hasenclever, Arunkumar Byravan, Gabriel Dulac-Arnold, Piotr Trochim, Nicolas Heess, Josh Merel, Yuval Tassa:
Learning Dynamics Models for Model Predictive Agents. CoRR abs/2109.14311 (2021) - 2020
- [c9]Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas:
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. NeurIPS 2020 - [c8]Antoine Marot, Benjamin Donnot, Gabriel Dulac-Arnold, Adrian Kelly, Aidan O'Sullivan, Jan Viebahn, Mariette Awad, Isabelle Guyon, Patrick Panciatici, Camilo Romero:
Learning to run a Power Network Challenge: a Retrospective Analysis. NeurIPS (Competition and Demos) 2020: 112-132 - [i14]Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, Todd Hester:
An empirical investigation of the challenges of real-world reinforcement learning. CoRR abs/2003.11881 (2020) - [i13]Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas:
RL Unplugged: Benchmarks for Offline Reinforcement Learning. CoRR abs/2006.13888 (2020) - [i12]Arthur Argenson, Gabriel Dulac-Arnold:
Model-Based Offline Planning. CoRR abs/2008.05556 (2020) - [i11]Cristian Bodnar, Karol Hausman, Gabriel Dulac-Arnold, Rico Jonschkowski:
A Geometric Perspective on Self-Supervised Policy Adaptation. CoRR abs/2011.07318 (2020)
2010 – 2019
- 2019
- [i10]Gabriel Dulac-Arnold, Daniel J. Mankowitz, Todd Hester:
Challenges of Real-World Reinforcement Learning. CoRR abs/1904.12901 (2019) - [i9]Gabriel Dulac-Arnold, Neil Zeghidour, Marco Cuturi, Lucas Beyer, Jean-Philippe Vert:
Deep multi-class learning from label proportions. CoRR abs/1905.12909 (2019) - [i8]Aude Genevay, Gabriel Dulac-Arnold, Jean-Philippe Vert:
Differentiable Deep Clustering with Cluster Size Constraints. CoRR abs/1910.09036 (2019) - 2018
- [c7]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, Joel Z. Leibo, Audrunas Gruslys:
Deep Q-learning From Demonstrations. AAAI 2018: 3223-3230 - 2017
- [c6]David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David P. Reichert, Neil C. Rabinowitz, André Barreto, Thomas Degris:
The Predictron: End-To-End Learning and Planning. ICML 2017: 3191-3199 - [i7]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:
Learning from Demonstrations for Real World Reinforcement Learning. CoRR abs/1704.03732 (2017) - 2016
- [i6]David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David P. Reichert, Neil C. Rabinowitz, André Barreto, Thomas Degris:
The Predictron: End-To-End Learning and Planning. CoRR abs/1612.08810 (2016) - 2015
- [i5]Peter Sunehag, Richard Evans, Gabriel Dulac-Arnold, Yori Zwols, Daniel Visentin, Ben Coppin:
Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions. CoRR abs/1512.01124 (2015) - [i4]Gabriel Dulac-Arnold, Richard Evans, Peter Sunehag, Ben Coppin:
Reinforcement Learning in Large Discrete Action Spaces. CoRR abs/1512.07679 (2015) - 2014
- [c5]Gabriel Dulac-Arnold, Ludovic Denoyer, Nicolas Thome, Matthieu Cord, Patrick Gallinari:
Sequentially Generated Instance-Dependent Image Representations for Classification. ICLR 2014 - 2012
- [j1]Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari:
Sequential approaches for learning datum-wise sparse representations. Mach. Learn. 89(1-2): 87-122 (2012) - [c4]Gabriel Dulac-Arnold, Ludovic Denoyer, Patrick Gallinari:
Lecture Séquentielle de Documents pour la Classification. CORIA 2012: 245-259 - [c3]Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari:
Fast Reinforcement Learning with Large Action Sets Using Error-Correcting Output Codes for MDP Factorization. ECML/PKDD (2) 2012: 180-194 - [i3]Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari:
Fast Reinforcement Learning with Large Action Sets using Error-Correcting Output Codes for MDP Factorization. CoRR abs/1203.0203 (2012) - 2011
- [c2]Gabriel Dulac-Arnold, Ludovic Denoyer, Patrick Gallinari:
Text Classification: A Sequential Reading Approach. ECIR 2011: 411-423 - [c1]Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari:
Datum-Wise Classification: A Sequential Approach to Sparsity. ECML/PKDD (1) 2011: 375-390 - [i2]Gabriel Dulac-Arnold, Ludovic Denoyer, Patrick Gallinari:
Text Classification: A Sequential Reading Approach. CoRR abs/1107.1322 (2011) - [i1]Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari:
Datum-Wise Classification: A Sequential Approach to Sparsity. CoRR abs/1108.5668 (2011)
Coauthor Index
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