Pierre-Luc Bacon

According to our database, Pierre-Luc Bacon authored at least 47 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

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

CoRR, 2024

Exploring Scaling Trends in LLM Robustness.

Nikolaus H. R. Howe, Michal Zajac, Ian R. McKenzie, Oskar Hollinsworth, Tom Tseng, Pierre-Luc Bacon, Adam Gleave

CoRR, 2024

Generative Active Learning for the Search of Small-molecule Protein Binders.

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

CoRR, 2024

Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons.

Simon Dufort-Labbé, Pierluca D'Oro, Evgenii Nikishin, Razvan Pascanu, Pierre-Luc Bacon, Aristide Baratin

CoRR, 2024

Do Transformer World Models Give Better Policy Gradients?

Michel Ma, Tianwei Ni, Clement Gehring, Pierluca D'Oro, Pierre-Luc Bacon

Proceedings of the Forty-first International Conference on Machine Learning, 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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Decoupling regularization from the action space.

Sobhan Mohammadpour, Emma Frejinger, Pierre-Luc Bacon

Proceedings of the Twelfth International Conference on Learning Representations, 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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Course Correcting Koopman Representations.

Mahan Fathi, Clement Gehring, Jonathan Pilault, David Kanaa, Pierre-Luc Bacon, Ross Goroshin

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Maximum entropy GFlowNets with soft Q-learning.

Sobhan Mohammadpour, Emmanuel Bengio, Emma Frejinger, Pierre-Luc Bacon

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Block-State Transformer.

Mahan Fathi, Jonathan Pilault, Pierre-Luc Bacon, Christopher Pal, Orhan Firat, Ross Goroshin

CoRR, 2023

Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design.

Julien Roy, Pierre-Luc Bacon, Christopher Pal, Emmanuel Bengio

CoRR, 2023

Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control.

Nate Rahn, Pierluca D'Oro, Harley Wiltzer, Pierre-Luc Bacon, Marc G. Bellemare

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Block-State Transformers.

Jonathan Pilault, Mahan Fathi, Orhan Firat, Chris Pal, Pierre-Luc Bacon, Ross Goroshin

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment.

Tianwei Ni, Michel Ma, Benjamin Eysenbach, Pierre-Luc Bacon

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Double Gumbel Q-Learning.

David Yu-Tung Hui, Aaron C. Courville, Pierre-Luc Bacon

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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 C. Courville

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization.

Leo Feng, Padideh Nouri, Aneri Muni, Yoshua Bengio, Pierre-Luc Bacon

CoRR, 2022

Myriad: a real-world testbed to bridge trajectory optimization and deep learning.

Nikolaus H. R. Howe, Simon Dufort-Labbé, Nitarshan Rajkumar, Pierre-Luc Bacon

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Direct Behavior Specification via Constrained Reinforcement Learning.

Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Christopher J. Pal

Proceedings of the International Conference on Machine Learning, 2022

The Primacy Bias in Deep Reinforcement Learning.

Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron C. Courville

Proceedings of the International Conference on Machine Learning, 2022

Continuous-Time Meta-Learning with Forward Mode Differentiation.

Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon

Proceedings of the Tenth International Conference on Learning Representations, 2022

Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation.

Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning.

Dilip Arumugam, Peter Henderson, Pierre-Luc Bacon

CoRR, 2021

Neural Algorithmic Reasoners are Implicit Planners.

Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

TDprop: Does Adaptive Optimization With Jacobi Preconditioning Help Temporal Difference Learning?

Joshua Romoff, Peter Henderson, David Kanaa, Emmanuel Bengio, Ahmed Touati, Pierre-Luc Bacon, Joelle Pineau

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

2020

XLVIN: eXecuted Latent Value Iteration Nets.

Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic

CoRR, 2020

Graph neural induction of value iteration.

Andreea Deac, Pierre-Luc Bacon, Jian Tang

CoRR, 2020

TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?

Joshua Romoff, Peter Henderson, David Kanaa, Emmanuel Bengio, Ahmed Touati, Pierre-Luc Bacon, Joelle Pineau

CoRR, 2020

Policy Evaluation Networks.

Jean Harb, Tom Schaul, Doina Precup, Pierre-Luc Bacon

CoRR, 2020

Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling.

Yao Liu, Pierre-Luc Bacon, Emma Brunskill

Proceedings of the 37th International Conference on Machine Learning, 2020

Options of Interest: Temporal Abstraction with Interest Functions.

Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods.

Riashat Islam, Raihan Seraj, Pierre-Luc Bacon, Doina Precup

CoRR, 2019

All-Action Policy Gradient Methods: A Numerical Integration Approach.

Benjamin Petit, Loren Amdahl-Culleton, Yao Liu, Jimmy Smith, Pierre-Luc Bacon

CoRR, 2019

2018

The Barbados 2018 List of Open Issues in Continual Learning.

Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc G. Bellemare, Doina Precup

CoRR, 2018

Constructing Temporal Abstractions Autonomously in Reinforcement Learning.

Pierre-Luc Bacon, Doina Precup

AI Mag., 2018

Convergent TREE BACKUP and RETRACE with Function Approximation.

Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent

Proceedings of the 35th International Conference on Machine Learning, 2018

Learning Robust Options.

Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Learning With Options That Terminate Off-Policy.

Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon, Doina Precup, Ann Nowé

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

When Waiting Is Not an Option: Learning Options With a Deliberation Cost.

Jean Harb, Pierre-Luc Bacon, Martin Klissarov, Doina Precup

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 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

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Learnings Options End-to-End for Continuous Action Tasks.

Martin Klissarov, Pierre-Luc Bacon, Jean Harb, Doina Precup

CoRR, 2017

The Option-Critic Architecture.

Pierre-Luc Bacon, Jean Harb, Doina Precup

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

A Matrix Splitting Perspective on Planning with Options.

Pierre-Luc Bacon, Doina Precup

CoRR, 2016

2015

Conditional Computation in Neural Networks for faster models.

Emmanuel Bengio, Pierre-Luc Bacon, Joelle Pineau, Doina Precup

CoRR, 2015

Learning and Planning with Timing Information in Markov Decision Processes.

Pierre-Luc Bacon, Borja Balle, Doina Precup

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Analyzing Open Data from the City of Montreal.

Joelle Pineau, Pierre-Luc Bacon

Proceedings of the 2nd International Workshop on Mining Urban Data co-located with 32nd International Conference on Machine Learning (ICML 2015), 2015