Lyle, Clare

30 publications

AISTATS 2025 A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Avila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana L Borsa, Arthur Guez, Will Dabney

NeurIPS 2025 Plasticity as the Mirror of Empowerment David Abel, Michael Bowling, Andre Barreto, Will Dabney, Shi Dong, Steven Stenberg Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh

NeurIPSW 2024 A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Avila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana L Borsa, Arthur Guez, Will Dabney

CoLLAs 2024 Disentangling the Causes of Plasticity Loss in Neural Networks Clare Lyle, Zeyu Zheng, Khimya Khetarpal, Hado van Hasselt, Razvan Pascanu, James Martens, Will Dabney

ICML 2024 Mixtures of Experts Unlock Parameter Scaling for Deep RL Johan Samir Obando Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob Nicolaus Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro

NeurIPS 2024 Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney

NeurIPS 2024 Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani

NeurIPS 2024 Normalization and Effective Learning Rates in Reinforcement Learning Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado van Hasselt, Razvan Pascanu, Will Dabney

ICML 2024 Slow and Steady Wins the Race: Maintaining Plasticity with Hare and Tortoise Networks Hojoon Lee, Hyeonseo Cho, Hyunseung Kim, Donghu Kim, Dugki Min, Jaegul Choo, Clare Lyle

NeurIPS 2023 Deep Reinforcement Learning with Plasticity Injection Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, Andre Barreto

ICLRW 2023 Deep Reinforcement Learning with Plasticity Injection Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, Andre Barreto

ICML 2023 DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab

ICML 2023 Quantile Credit Assignment Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber, Clare Lyle, Audrunas Gruslys, Michal Valko, Will Dabney, Georg Ostrovski, Eric Moulines, Remi Munos

ICML 2023 The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation Mark Rowland, Yunhao Tang, Clare Lyle, Remi Munos, Marc G Bellemare, Will Dabney

ICML 2023 Understanding Plasticity in Neural Networks Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Avila Pires, Razvan Pascanu, Will Dabney

ICML 2023 Understanding Self-Predictive Learning for Reinforcement Learning Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Avila Pires, Yash Chandak, Remi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko

NeurIPSW 2023 Vision-Language Models as a Source of Rewards Kate Baumli, Satinder Singh, Feryal Behbahani, Harris Chan, Gheorghe Comanici, Sebastian Flennerhag, Maxime Gazeau, Kristian Holsheimer, Dan Horgan, Michael Laskin, Clare Lyle, Volodymyr Mnih, Alexander Neitz, Fabio Pardo, Jack Parker-Holder, John Quan, Tim Rocktäschel, Himanshu Sahni, Tom Schaul, Yannick Schroecker, Stephen Spencer, Richie Steigerwald, Luyu Wang, Lei M Zhang

ICML 2022 Learning Dynamics and Generalization in Deep Reinforcement Learning Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal

ICLR 2022 Understanding and Preventing Capacity Loss in Reinforcement Learning Clare Lyle, Mark Rowland, Will Dabney

AISTATS 2021 On the Effect of Auxiliary Tasks on Representation Dynamics Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney

NeurIPSW 2021 DARTS Without a Validation Set: Optimizing the Marginal Likelihood Miroslav Fil, Binxin Ru, Clare Lyle, Yarin Gal

IJCAI 2021 Provable Guarantees on the Robustness of Decision Rules to Causal Interventions Benjie Wang, Clare Lyle, Marta Kwiatkowska

ICML 2021 PsiPhi-Learning: Reinforcement Learning with Demonstrations Using Successor Features and Inverse Temporal Difference Learning Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar

NeurIPS 2021 Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning Jannik Kossen, Neil Band, Clare Lyle, Aidan N Gomez, Thomas Rainforth, Yarin Gal

NeurIPS 2021 Speedy Performance Estimation for Neural Architecture Search Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal

NeurIPSW 2021 Understanding and Preventing Capacity Loss in Reinforcement Learning Clare Lyle, Mark Rowland, Will Dabney

NeurIPS 2020 A Bayesian Perspective on Training Speed and Model Selection Clare Lyle, Lisa Schut, Robin Ru, Yarin Gal, Mark van der Wilk

ICML 2020 Invariant Causal Prediction for Block MDPs Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup

AAAI 2019 A Comparative Analysis of Expected and Distributional Reinforcement Learning Clare Lyle, Marc G. Bellemare, Pablo Samuel Castro

NeurIPS 2019 A Geometric Perspective on Optimal Representations for Reinforcement Learning Marc Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle