Clare Lyle

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 50

Top Topics

Temporal Difference

Reinforcement Learning

Pitman Yor Process

Top Venues

Publications

Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Ávila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana Borsa, Arthur Guez, Will Dabney
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. CoRR (2024)
Hojoon Lee, Hyeonseo Cho, Hyunseung Kim, Donghu Kim, Dugki Min, Jaegul Choo, Clare Lyle
Slow and Steady Wins the Race: Maintaining Plasticity with Hare and Tortoise Networks. CoRR (2024)
Augustine N. Mavor-Parker, Matthew J. Sargent, Caswell Barry, Lewis D. Griffin, Clare Lyle
Frequency and Generalisation of Periodic Activation Functions in Reinforcement Learning. CoRR (2024)
Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. CoRR (2024)
Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado van Hasselt, Razvan Pascanu, Will Dabney
Normalization and effective learning rates in reinforcement learning. CoRR (2024)
Johan S. Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob N. Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro
Mixtures of Experts Unlock Parameter Scaling for Deep RL. CoRR (2024)
Clare Lyle, Zeyu Zheng, Khimya Khetarpal, Hado van Hasselt, Razvan Pascanu, James Martens, Will Dabney
Disentangling the Causes of Plasticity Loss in Neural Networks. CoRR (2024)
Mohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam Mahmood
Weight Clipping for Deep Continual and Reinforcement Learning. CoRR (2024)
Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. CoRR (2023)
Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney
Understanding plasticity in neural networks. CoRR (2023)
Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab
DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design. CoRR (2023)
Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto
Deep Reinforcement Learning with Plasticity Injection. CoRR (2023)
Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. ICML (2023)
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko
Understanding Self-Predictive Learning for Reinforcement Learning. ICML (2023)
Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber, Clare Lyle, Audrunas Gruslys, Michal Valko, Will Dabney, Georg Ostrovski, Eric Moulines, Rémi Munos
Quantile Credit Assignment. ICML (2023)
Kate Baumli, Satinder Baveja, Feryal M. P. Behbahani, Harris Chan, Gheorghe Comanici, Sebastian Flennerhag, Maxime Gazeau, Kristian Holsheimer, Dan Horgan, Michael Laskin, Clare Lyle, Hussain Masoom, Kay McKinney, 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 Zhang
Vision-Language Models as a Source of Rewards. CoRR (2023)
Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab
DiscoBAX: Discovery of optimal intervention sets in genomic experiment design. ICML (2023)
Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto
Deep Reinforcement Learning with Plasticity Injection. NeurIPS (2023)
Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney
Understanding Plasticity in Neural Networks. ICML (2023)
Clare Lyle, Mark Rowland, Will Dabney
Understanding and Preventing Capacity Loss in Reinforcement Learning. CoRR (2022)
Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
Learning Dynamics and Generalization in Reinforcement Learning. CoRR (2022)
Clare Lyle
Generalization Through the Lens of Learning Dynamics. CoRR (2022)
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko
Understanding Self-Predictive Learning for Reinforcement Learning. CoRR (2022)
Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
Learning Dynamics and Generalization in Deep Reinforcement Learning. ICML (2022)
Clare Lyle, Mark Rowland, Will Dabney
Understanding and Preventing Capacity Loss in Reinforcement Learning. ICLR (2022)
Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Tom Rainforth, Yarin Gal
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning. CoRR (2021)
Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney
On the Effect of Auxiliary Tasks on Representation Dynamics. AISTATS (2021)
Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Thomas Rainforth, Yarin Gal
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning. NeurIPS (2021)
Benjie Wang, Clare Lyle, Marta Kwiatkowska
Provable Guarantees on the Robustness of Decision Rules to Causal Interventions. IJCAI (2021)
Miroslav Fil, Binxin Ru, Clare Lyle, Yarin Gal
DARTS without a Validation Set: Optimizing the Marginal Likelihood. CoRR (2021)
Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. ICML (2021)
Benjie Wang, Clare Lyle, Marta Kwiatkowska
Provable Guarantees on the Robustness of Decision Rules to Causal Interventions. CoRR (2021)
Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. CoRR (2021)
Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal
Speedy Performance Estimation for Neural Architecture Search. NeurIPS (2021)
Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney
On The Effect of Auxiliary Tasks on Representation Dynamics. CoRR (2021)
Lorenz Kuhn, Clare Lyle, Aidan N. Gomez, Jonas Rothfuss, Yarin Gal
Robustness to Pruning Predicts Generalization in Deep Neural Networks. CoRR (2021)
Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk
A Bayesian Perspective on Training Speed and Model Selection. CoRR (2020)
Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal, Benjamin Bloem-Reddy
On the Benefits of Invariance in Neural Networks. CoRR (2020)
Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, Yarin Gal
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search. CoRR (2020)
Clare Lyle, Lisa Schut, Robin Ru, Yarin Gal, Mark van der Wilk
A Bayesian Perspective on Training Speed and Model Selection. NeurIPS (2020)
Andreas Kirsch, Clare Lyle, Yarin Gal
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning. CoRR (2020)
Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup
Invariant Causal Prediction for Block MDPs. CoRR (2020)
Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup
Invariant Causal Prediction for Block MDPs. ICML (2020)
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
A Geometric Perspective on Optimal Representations for Reinforcement Learning. NeurIPS (2019)
Clare Lyle, Pablo Samuel Castro, Marc G. Bellemare
A Comparative Analysis of Expected and Distributional Reinforcement Learning. CoRR (2019)
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
A Geometric Perspective on Optimal Representations for Reinforcement Learning. CoRR (2019)
Clare Lyle, Marc G. Bellemare, Pablo Samuel Castro
A Comparative Analysis of Expected and Distributional Reinforcement Learning. AAAI (2019)
Thang Doan, Bogdan Mazoure, Clare Lyle
GAN Q-learning. CoRR (2018)
Miles Brundage, Shahar Avin, Jack Clark, Helen Toner, Peter Eckersley, Ben Garfinkel, Allan Dafoe, Paul Scharre, Thomas Zeitzoff, Bobby Filar, Hyrum S. Anderson, Heather Roff, Gregory C. Allen, Jacob Steinhardt, Carrick Flynn, Seán Ó hÉigeartaigh, Simon Beard, Haydn Belfield, Sebastian Farquhar, Clare Lyle, Rebecca Crootof, Owain Evans, Michael Page, Joanna Bryson, Roman Yampolskiy, Dario Amodei
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. CoRR (2018)
Cody Barnson, Dawn Chandler, Qiao Chen, Christina Chung, Andrew Coccimiglio, Sean La, Lily Li, Aïna Linn, Anna Lubiw, Clare Lyle, Shikha Mahajan, Gregory W. Mierzwinski, Simon Pratt, Yoon Su Yoo, Hongbo Zhang, Kevin Zhang
Some Counterexamples for Compatible Triangulations. CoRR (2016)