Bacon, Pierre-Luc

43 publications

ICLR 2026 Discrete Compositional Generation via General Soft Operators and Robust Reinforcement Learning Marco Jiralerspong, Esther Derman, Danilo Vucetic, Nikolay Malkin, Bilun Sun, Tianyu Zhang, Pierre-Luc Bacon, Gauthier Gidel

ICLR 2025 MaestroMotif: Skill Design from Artificial Intelligence Feedback Martin Klissarov, Mikael Henaff, Roberta Raileanu, Shagun Sodhani, Pascal Vincent, Amy Zhang, Pierre-Luc Bacon, Doina Precup, Marlos C. Machado, Pierluca D'Oro

TMLR 2025 Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons Simon Dufort-Labbé, Pierluca D'Oro, Evgenii Nikishin, Irina Rish, Pierre-Luc Bacon, Razvan Pascanu, Aristide Baratin

ICLRW 2025 Mol-MoE: Training Preference-Guided Routers for Molecule Generation Diego Calanzone, Pierluca D'Oro, Pierre-Luc Bacon

ICML 2025 Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Guozheng Ma, Lu Li, Zilin Wang, Li Shen, Pierre-Luc Bacon, Dacheng Tao

ICML 2025 Scaling Trends in Language Model Robustness Nikolaus H. R. Howe, Ian R. Mckenzie, Oskar John Hollinsworth, Michał Zając, Tom Tseng, Aaron David Tucker, Pierre-Luc Bacon, Adam Gleave

NeurIPS 2025 Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning Roger Creus Castanyer, Johan Obando-Ceron, Lu Li, Pierre-Luc Bacon, Glen Berseth, Aaron Courville, Pablo Samuel Castro

NeurIPS 2025 State Entropy Regularization for Robust Reinforcement Learning Yonatan Ashlag, Uri Koren, Mirco Mutti, Esther Derman, Pierre-Luc Bacon, Shie Mannor

ICLR 2024 Bridging State and History Representations: Understanding Self-Predictive RL Tianwei Ni, Benjamin Eysenbach, Erfan SeyedSalehi, Michel Ma, Clement Gehring, Aditya Mahajan, Pierre-Luc Bacon

ICLR 2024 Course Correcting Koopman Representations Mahan Fathi, Clement Gehring, Jonathan Pilault, David Kanaa, Pierre-Luc Bacon, Ross Goroshin

ICLR 2024 Decoupling Regularization from the Action Space Sobhan Mohammadpour, Emma Frejinger, Pierre-Luc Bacon

ICML 2024 Do Transformer World Models Give Better Policy Gradients? Michel Ma, Tianwei Ni, Clement Gehring, Pierluca D’Oro, Pierre-Luc Bacon

ICMLW 2024 Exploring Scaling Trends in LLM Robustness Nikolaus H. R. Howe, Michał Zając, Ian R. McKenzie, Oskar John Hollinsworth, Pierre-Luc Bacon, Adam Gleave

AISTATS 2024 Maximum Entropy GFlowNets with Soft Q-Learning Sobhan Mohammadpour, Emmanuel Bengio, Emma Frejinger, Pierre-Luc Bacon

ICLR 2024 Motif: Intrinsic Motivation from Artificial Intelligence Feedback Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff

NeurIPS 2023 Block-State Transformers Jonathan Pilault, Mahan Fathi, Orhan Firat, Chris Pal, Pierre-Luc Bacon, Ross Goroshin

NeurIPS 2023 Double Gumbel Q-Learning David Yu-Tung Hui, Aaron C. Courville, Pierre-Luc Bacon

ICMLW 2023 Goal-Conditioned GFlowNets for Controllable Multi-Objective Molecular Design Julien Roy, Pierre-Luc Bacon, Christopher Pal, Emmanuel Bengio

NeurIPSW 2023 Motif: Intrinsic Motivation from Artificial Intelligence Feedback Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff

NeurIPSW 2023 Motif: Intrinsic Motivation from Artificial Intelligence Feedback Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff

NeurIPS 2023 Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control Nate Rahn, Pierluca D'Oro, Harley Wiltzer, Pierre-Luc Bacon, Marc Bellemare

ICLR 2023 Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G Bellemare, Aaron Courville

NeurIPS 2023 When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment Tianwei Ni, Michel Ma, Benjamin Eysenbach, Pierre-Luc Bacon

NeurIPSW 2023 When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment Tianwei Ni, Michel Ma, Benjamin Eysenbach, Pierre-Luc Bacon

ICLR 2022 Continuous-Time Meta-Learning with Forward Mode Differentiation Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon

AAAI 2022 Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon

ICML 2022 Direct Behavior Specification via Constrained Reinforcement Learning Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Chris J Pal

NeurIPS 2022 Myriad: A Real-World Testbed to Bridge Trajectory Optimization and Deep Learning Nikolaus Howe, Simon Dufort-Labbé, Nitarshan Rajkumar, Pierre-Luc Bacon

NeurIPSW 2022 Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G Bellemare, Aaron Courville

ICML 2022 The Primacy Bias in Deep Reinforcement Learning Evgenii Nikishin, Max Schwarzer, Pierluca D’Oro, Pierre-Luc Bacon, Aaron Courville

ICMLW 2021 Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon

NeurIPSW 2021 Long-Term Credit Assignment via Model-Based Temporal Shortcuts Michel Ma, Pierluca D'Oro, Yoshua Bengio, Pierre-Luc Bacon

NeurIPS 2021 Neural Algorithmic Reasoners Are Implicit Planners Andreea-Ioana Deac, Petar Veličković, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic

AAAI 2020 Options of Interest: Temporal Abstraction with Interest Functions Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup

ICML 2020 Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling Yao Liu, Pierre-Luc Bacon, Emma Brunskill

NeurIPSW 2020 XLVIN: eXecuted Latent Value Iteration Nets Andreea Deac, Petar Veličković, Ognjen Milinković, Pierre-Luc Bacon, Jian Tang, Mladen Nikolić

ICML 2018 Convergent Tree Backup and Retrace with Function Approximation Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent

AAAI 2018 Learning Robust Options Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor

AAAI 2018 Learning with Options That Terminate Off-Policy Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon, Doina Precup, Ann Nowé

AAAI 2018 OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup

AAAI 2018 When Waiting Is Not an Option: Learning Options with a Deliberation Cost Jean Harb, Pierre-Luc Bacon, Martin Klissarov, Doina Precup

AAAI 2017 The Option-Critic Architecture Pierre-Luc Bacon, Jean Harb, Doina Precup

UAI 2015 Learning and Planning with Timing Information in Markov Decision Processes Pierre-Luc Bacon, Borja Balle, Doina Precup