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2020 – today
- 2024
[c28]Sobhan Mohammadpour, Emmanuel Bengio, Emma Frejinger, Pierre-Luc Bacon:
Maximum entropy GFlowNets with soft Q-learning. AISTATS 2024: 2593-2601- [c27]Mahan Fathi, Clement Gehring, Jonathan Pilault, David Kanaa, Pierre-Luc Bacon, Ross Goroshin:
Course Correcting Koopman Representations. ICLR 2024 - [c26]Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff:
Motif: Intrinsic Motivation from Artificial Intelligence Feedback. ICLR 2024 - [c25]Sobhan Mohammadpour, Emma Frejinger, Pierre-Luc Bacon:
Decoupling regularization from the action space. ICLR 2024 - [c24]Tianwei Ni, Benjamin Eysenbach, Erfan Seyedsalehi, Michel Ma, Clement Gehring, Aditya Mahajan, Pierre-Luc Bacon:
Bridging State and History Representations: Understanding Self-Predictive RL. ICLR 2024 - [c23]Michel Ma, Tianwei Ni, Clement Gehring, Pierluca D'Oro, Pierre-Luc Bacon:
Do Transformer World Models Give Better Policy Gradients? ICML 2024 - [i39]Tianwei Ni, Benjamin Eysenbach
, Erfan Seyedsalehi, Michel Ma, Clement Gehring, Aditya Mahajan, Pierre-Luc Bacon:
Bridging State and History Representations: Understanding Self-Predictive RL. CoRR abs/2401.08898 (2024) - [i38]Michel Ma, Tianwei Ni, Clement Gehring, Pierluca D'Oro, Pierre-Luc Bacon:
Do Transformer World Models Give Better Policy Gradients? CoRR abs/2402.05290 (2024) - [i37]Simon Dufort-Labbé, Pierluca D'Oro, Evgenii Nikishin, Razvan Pascanu, Pierre-Luc Bacon, Aristide Baratin:
Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons. CoRR abs/2403.07688 (2024) - [i36]Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, Sai Krishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampásek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin H. S. Segler, Michael M. Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio:
Generative Active Learning for the Search of Small-molecule Protein Binders. CoRR abs/2405.01616 (2024) - [i35]Sobhan Mohammadpour, Emma Frejinger, Pierre-Luc Bacon:
Decoupling regularization from the action space. CoRR abs/2406.05953 (2024) - [i34]Nikolaus H. R. Howe, Michal Zajac, Ian R. McKenzie, Oskar Hollinsworth, Tom Tseng, Pierre-Luc Bacon, Adam Gleave:
Exploring Scaling Trends in LLM Robustness. CoRR abs/2407.18213 (2024) - 2023
- [c22]Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G. Bellemare, Aaron C. Courville:
Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier. ICLR 2023 - [c21]David Yu-Tung Hui, Aaron C. Courville, Pierre-Luc Bacon:
Double Gumbel Q-Learning. NeurIPS 2023 - [c20]Tianwei Ni, Michel Ma, Benjamin Eysenbach, Pierre-Luc Bacon:
When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment. NeurIPS 2023 - [c19]Jonathan Pilault, Mahan Fathi, Orhan Firat, Chris Pal, Pierre-Luc Bacon, Ross Goroshin:
Block-State Transformers. NeurIPS 2023 - [c18]Nate Rahn, Pierluca D'Oro, Harley Wiltzer, Pierre-Luc Bacon, Marc G. Bellemare:
Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control. NeurIPS 2023 - [i33]Julien Roy, Pierre-Luc Bacon, Christopher Pal, Emmanuel Bengio:
Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design. CoRR abs/2306.04620 (2023) - [i32]Mahan Fathi, Jonathan Pilault, Pierre-Luc Bacon, Christopher Pal, Orhan Firat, Ross Goroshin:
Block-State Transformer. CoRR abs/2306.09539 (2023) - [i31]Tianwei Ni, Michel Ma, Benjamin Eysenbach, Pierre-Luc Bacon:
When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment. CoRR abs/2307.03864 (2023) - [i30]Nate Rahn, Pierluca D'Oro, Harley Wiltzer, Pierre-Luc Bacon, Marc G. Bellemare:
Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control. CoRR abs/2309.14597 (2023) - [i29]Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff:
Motif: Intrinsic Motivation from Artificial Intelligence Feedback. CoRR abs/2310.00166 (2023) - [i28]Mahan Fathi, Clement Gehring, Jonathan Pilault, David Kanaa, Pierre-Luc Bacon, Ross Goroshin:
Course Correcting Koopman Representations. CoRR abs/2310.15386 (2023) - [i27]Sobhan Mohammadpour, Emmanuel Bengio, Emma Frejinger, Pierre-Luc Bacon:
Maximum entropy GFlowNets with soft Q-learning. CoRR abs/2312.14331 (2023) - 2022
- [c17]Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon:
Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation. AAAI 2022: 7886-7894 - [c16]Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon:
Continuous-Time Meta-Learning with Forward Mode Differentiation. ICLR 2022 - [c15]Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron C. Courville:
The Primacy Bias in Deep Reinforcement Learning. ICML 2022: 16828-16847 - [c14]Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Christopher J. Pal:
Direct Behavior Specification via Constrained Reinforcement Learning. ICML 2022: 18828-18843 - [c13]Nikolaus H. R. Howe, Simon Dufort-Labbé, Nitarshan Rajkumar, Pierre-Luc Bacon:
Myriad: a real-world testbed to bridge trajectory optimization and deep learning. NeurIPS 2022 - [i26]Nikolaus H. R. Howe, Simon Dufort-Labbé, Nitarshan Rajkumar, Pierre-Luc Bacon:
Myriad: a real-world testbed to bridge trajectory optimization and deep learning. CoRR abs/2202.10600 (2022) - [i25]Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon:
Continuous-Time Meta-Learning with Forward Mode Differentiation. CoRR abs/2203.01443 (2022) - [i24]Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron C. Courville:
The Primacy Bias in Deep Reinforcement Learning. CoRR abs/2205.07802 (2022) - [i23]Leo Feng, Padideh Nouri, Aneri Muni, Yoshua Bengio, Pierre-Luc Bacon:
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization. CoRR abs/2209.06259 (2022) - 2021
- [c12]Joshua Romoff, Peter Henderson, David Kanaa, Emmanuel Bengio, Ahmed Touati, Pierre-Luc Bacon, Joelle Pineau:
TDprop: Does Adaptive Optimization With Jacobi Preconditioning Help Temporal Difference Learning? AAMAS 2021: 1082-1090 - [c11]Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic:
Neural Algorithmic Reasoners are Implicit Planners. NeurIPS 2021: 15529-15542 - [i22]Dilip Arumugam, Peter Henderson, Pierre-Luc Bacon:
An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning. CoRR abs/2103.06224 (2021) - [i21]Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon:
Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation. CoRR abs/2106.03273 (2021) - [i20]Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic:
Neural Algorithmic Reasoners are Implicit Planners. CoRR abs/2110.05442 (2021) - [i19]Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Christopher J. Pal:
Direct Behavior Specification via Constrained Reinforcement Learning. CoRR abs/2112.12228 (2021) - 2020
- [c10]Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup:
Options of Interest: Temporal Abstraction with Interest Functions. AAAI 2020: 4444-4451 - [c9]Yao Liu, Pierre-Luc Bacon, Emma Brunskill:
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling. ICML 2020: 6184-6193 - [i18]Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup:
Options of Interest: Temporal Abstraction with Interest Functions. CoRR abs/2001.00271 (2020) - [i17]Jean Harb, Tom Schaul, Doina Precup, Pierre-Luc Bacon:
Policy Evaluation Networks. CoRR abs/2002.11833 (2020) - [i16]Joshua Romoff, Peter Henderson, David Kanaa, Emmanuel Bengio, Ahmed Touati, Pierre-Luc Bacon, Joelle Pineau:
TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning? CoRR abs/2007.02786 (2020) - [i15]Andreea Deac, Pierre-Luc Bacon, Jian Tang:
Graph neural induction of value iteration. CoRR abs/2009.12604 (2020) - [i14]Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic:
XLVIN: eXecuted Latent Value Iteration Nets. CoRR abs/2010.13146 (2020)
2010 – 2019
- 2019
- [i13]Yao Liu, Pierre-Luc Bacon, Emma Brunskill:
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling. CoRR abs/1910.06508 (2019) - [i12]Benjamin Petit, Loren Amdahl-Culleton, Yao Liu, Jimmy Smith, Pierre-Luc Bacon:
All-Action Policy Gradient Methods: A Numerical Integration Approach. CoRR abs/1910.09093 (2019) - [i11]Riashat Islam, Raihan Seraj, Pierre-Luc Bacon, Doina Precup:
Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods. CoRR abs/1912.05104 (2019) - 2018
- [j1]Pierre-Luc Bacon, Doina Precup:
Constructing Temporal Abstractions Autonomously in Reinforcement Learning. AI Mag. 39(1): 39-50 (2018) - [c8]Jean Harb, Pierre-Luc Bacon, Martin Klissarov, Doina Precup:
When Waiting Is Not an Option: Learning Options With a Deliberation Cost. AAAI 2018: 3165-3172 - [c7]Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon, Doina Precup, Ann Nowé:
Learning With Options That Terminate Off-Policy. AAAI 2018: 3173-3182 - [c6]Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup:
OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning. AAAI 2018: 3199-3206 - [c5]Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor:
Learning Robust Options. AAAI 2018: 6409-6416 - [c4]Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent:
Convergent TREE BACKUP and RETRACE with Function Approximation. ICML 2018: 4962-4971 - [i10]Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor:
Learning Robust Options. CoRR abs/1802.03236 (2018) - [i9]Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc G. Bellemare, Doina Precup:
The Barbados 2018 List of Open Issues in Continual Learning. CoRR abs/1811.07004 (2018) - 2017
- [c3]Pierre-Luc Bacon, Jean Harb, Doina Precup:
The Option-Critic Architecture. AAAI 2017: 1726-1734 - [i8]Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent:
Convergent Tree-Backup and Retrace with Function Approximation. CoRR abs/1705.09322 (2017) - [i7]Jean Harb, Pierre-Luc Bacon, Martin Klissarov, Doina Precup:
When Waiting is not an Option : Learning Options with a Deliberation Cost. CoRR abs/1709.04571 (2017) - [i6]Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup:
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning. CoRR abs/1709.06683 (2017) - [i5]Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon, Doina Precup, Ann Nowé:
Learning with Options that Terminate Off-Policy. CoRR abs/1711.03817 (2017) - [i4]Martin Klissarov, Pierre-Luc Bacon, Jean Harb, Doina Precup:
Learnings Options End-to-End for Continuous Action Tasks. CoRR abs/1712.00004 (2017) - 2016
- [i3]Pierre-Luc Bacon, Jean Harb, Doina Precup:
The Option-Critic Architecture. CoRR abs/1609.05140 (2016) - [i2]Pierre-Luc Bacon, Doina Precup:
A Matrix Splitting Perspective on Planning with Options. CoRR abs/1612.00916 (2016) - 2015
- [c2]Joelle Pineau, Pierre-Luc Bacon:
Analyzing Open Data from the City of Montreal. MUD@ICML 2015: 11-16 - [c1]Pierre-Luc Bacon, Borja Balle, Doina Precup:
Learning and Planning with Timing Information in Markov Decision Processes. UAI 2015: 111-120 - [i1]Emmanuel Bengio, Pierre-Luc Bacon, Joelle Pineau, Doina Precup:
Conditional Computation in Neural Networks for faster models. CoRR abs/1511.06297 (2015)
Coauthor Index
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