Clare Lyle - RankMe

66 papers A* 21A 2Journal 42Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments. CoRR Michael Beukman, Khimya Khetarpal, Zeyu Zheng, Will Dabney, Jakob N. Foerster, Michael Dennis, Clare Lyle
2025	A	`conf`	A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. AISTATS Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Ávila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana L. Borsa, Arthur Guez, Will Dabney
2025	J	`jnl`	Agency Is Frame-Dependent. CoRR David Abel, André Barreto, Michael Bowling, Will Dabney, Shi Dong, Steven Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh
2025	J	`jnl`	Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning. CoRR Rafal Surdej, Michal Bortkiewicz, Alex Lewandowski, Mateusz Ostaszewski, Clare Lyle
2025	J	`jnl`	Fine-Tuned In-Context Learners for Efficient Adaptation. CoRR Jörg Bornschein, Clare Lyle, Yazhe Li, Amal Rannen-Triki, Xu Owen He, Razvan Pascanu
2025	J	`jnl`	Optimizers Qualitatively Alter Solutions And We Should Leverage This. CoRR Razvan Pascanu, Clare Lyle, Ionut-Vlad Modoranu, Naima Elosegui Borras, Dan Alistarh, Petar Velickovic, Sarath Chandar, Soham De, James Martens
2025	J	`jnl`	Plasticity as the Mirror of Empowerment. CoRR David Abel, Michael Bowling, André Barreto, Will Dabney, Shi Dong, Steven Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh
2025	J	`jnl`	What Can Grokking Teach Us About Learning Under Nonstationarity? CoRR Clare Lyle, Ghada Sokar, Razvan Pascanu, András György
2024	J	`jnl`	A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. CoRR Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Ávila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana Borsa, Arthur Guez, Will Dabney
2024	—	`conf`	Disentangling the Causes of Plasticity Loss in Neural Networks. CoLLAs Clare Lyle, Zeyu Zheng, Khimya Khetarpal, Hado van Hasselt, Razvan Pascanu, James Martens, Will Dabney
2024	J	`jnl`	Disentangling the Causes of Plasticity Loss in Neural Networks. CoRR Clare Lyle, Zeyu Zheng, Khimya Khetarpal, Hado van Hasselt, Razvan Pascanu, James Martens, Will Dabney
2024	J	`jnl`	Frequency and Generalisation of Periodic Activation Functions in Reinforcement Learning. CoRR Augustine N. Mavor-Parker, Matthew J. Sargent, Caswell Barry, Lewis D. Griffin, Clare Lyle
2024	A*	`conf`	Mixtures of Experts Unlock Parameter Scaling for Deep RL. ICML Johan S. Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob Nicolaus Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro
2024	J	`jnl`	Mixtures of Experts Unlock Parameter Scaling for Deep RL. CoRR Johan S. Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob N. Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro
2024	A*	`conf`	Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. NeurIPS Mark Rowland, Kevin Kevin Li, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney
2024	J	`jnl`	Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. CoRR Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney
2024	A*	`conf`	Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset. NeurIPS Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani
2024	J	`jnl`	Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset. CoRR Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani
2024	A*	`conf`	Normalization and effective learning rates in reinforcement learning. NeurIPS Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado Philip van Hasselt, Razvan Pascanu, Will Dabney
2024	J	`jnl`	Normalization and effective learning rates in reinforcement learning. CoRR Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado van Hasselt, Razvan Pascanu, Will Dabney
2024	A*	`conf`	Slow and Steady Wins the Race: Maintaining Plasticity with Hare and Tortoise Networks. ICML Hojoon Lee, Hyeonseo Cho, Hyunseung Kim, Donghu Kim, Dugki Min, Jaegul Choo, Clare Lyle
2024	J	`jnl`	Slow and Steady Wins the Race: Maintaining Plasticity with Hare and Tortoise Networks. CoRR Hojoon Lee, Hyeonseo Cho, Hyunseung Kim, Donghu Kim, Dugki Min, Jaegul Choo, Clare Lyle
2024	J	`jnl`	Weight Clipping for Deep Continual and Reinforcement Learning. RLJ Mohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam Mahmood
2024	J	`jnl`	Weight Clipping for Deep Continual and Reinforcement Learning. CoRR Mohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam Mahmood
2023	A*	`conf`	Deep Reinforcement Learning with Plasticity Injection. NeurIPS Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto
2023	J	`jnl`	Deep Reinforcement Learning with Plasticity Injection. CoRR Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto
2023	J	`jnl`	DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design. CoRR Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab
2023	A*	`conf`	DiscoBAX: Discovery of optimal intervention sets in genomic experiment design. ICML Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab
2023	A*	`conf`	Quantile Credit Assignment. ICML 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
2023	A*	`conf`	The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. ICML Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
2023	J	`jnl`	The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. CoRR Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
2023	A*	`conf`	Understanding Plasticity in Neural Networks. ICML Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney
2023	A*	`conf`	Understanding Self-Predictive Learning for Reinforcement Learning. ICML 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
2023	J	`jnl`	Understanding plasticity in neural networks. CoRR Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney
2023	J	`jnl`	Vision-Language Models as a Source of Rewards. CoRR 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
2022	J	`jnl`	Generalization Through the Lens of Learning Dynamics. CoRR Clare Lyle
2022	A*	`conf`	Learning Dynamics and Generalization in Deep Reinforcement Learning. ICML Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
2022	J	`jnl`	Learning Dynamics and Generalization in Reinforcement Learning. CoRR Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
2022	J	`jnl`	Understanding Self-Predictive Learning for Reinforcement Learning. CoRR 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
2022	A*	`conf`	Understanding and Preventing Capacity Loss in Reinforcement Learning. ICLR Clare Lyle, Mark Rowland, Will Dabney
2022	J	`jnl`	Understanding and Preventing Capacity Loss in Reinforcement Learning. CoRR Clare Lyle, Mark Rowland, Will Dabney
2021	J	`jnl`	DARTS without a Validation Set: Optimizing the Marginal Likelihood. CoRR Miroslav Fil, Binxin Ru, Clare Lyle, Yarin Gal
2021	J	`jnl`	On The Effect of Auxiliary Tasks on Representation Dynamics. CoRR Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney
2021	A	`conf`	On the Effect of Auxiliary Tasks on Representation Dynamics. AISTATS Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney
2021	A*	`conf`	Provable Guarantees on the Robustness of Decision Rules to Causal Interventions. IJCAI Benjie Wang, Clare Lyle, Marta Kwiatkowska
2021	J	`jnl`	Provable Guarantees on the Robustness of Decision Rules to Causal Interventions. CoRR Benjie Wang, Clare Lyle, Marta Kwiatkowska
2021	A*	`conf`	PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. ICML Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar
2021	J	`jnl`	PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. CoRR Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar
2021	J	`jnl`	Robustness to Pruning Predicts Generalization in Deep Neural Networks. CoRR Lorenz Kuhn, Clare Lyle, Aidan N. Gomez, Jonas Rothfuss, Yarin Gal
2021	A*	`conf`	Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning. NeurIPS Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Thomas Rainforth, Yarin Gal
2021	J	`jnl`	Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning. CoRR Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Tom Rainforth, Yarin Gal
2021	A*	`conf`	Speedy Performance Estimation for Neural Architecture Search. NeurIPS Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal
2020	A*	`conf`	A Bayesian Perspective on Training Speed and Model Selection. NeurIPS Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk
2020	J	`jnl`	A Bayesian Perspective on Training Speed and Model Selection. CoRR Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk
2020	A*	`conf`	Invariant Causal Prediction for Block MDPs. ICML Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup
2020	J	`jnl`	Invariant Causal Prediction for Block MDPs. CoRR Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup
2020	J	`jnl`	On the Benefits of Invariance in Neural Networks. CoRR Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal, Benjamin Bloem-Reddy
2020	J	`jnl`	Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search. CoRR Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, Yarin Gal
2020	J	`jnl`	Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning. CoRR Andreas Kirsch, Clare Lyle, Yarin Gal
2019	A*	`conf`	A Comparative Analysis of Expected and Distributional Reinforcement Learning. AAAI Clare Lyle, Marc G. Bellemare, Pablo Samuel Castro
2019	J	`jnl`	A Comparative Analysis of Expected and Distributional Reinforcement Learning. CoRR Clare Lyle, Pablo Samuel Castro, Marc G. Bellemare
2019	A*	`conf`	A Geometric Perspective on Optimal Representations for Reinforcement Learning. NeurIPS Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
2019	J	`jnl`	A Geometric Perspective on Optimal Representations for Reinforcement Learning. CoRR Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
2018	J	`jnl`	GAN Q-learning. CoRR Thang Doan, Bogdan Mazoure, Clare Lyle
2018	J	`jnl`	The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. CoRR 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
2016	J	`jnl`	Some Counterexamples for Compatible Triangulations. CoRR 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