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ORCIDClare Lyle
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2020 – today
- 2024
[j1]Mohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam Mahmood:
Weight Clipping for Deep Continual and Reinforcement Learning. RLJ 5: 2198-2217 (2024)- [c19]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. ICML 2024 - [c18]Johan Samir Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob Nicolaus Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro:
Mixtures of Experts Unlock Parameter Scaling for Deep RL. ICML 2024 - [i33]Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney:
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. CoRR abs/2402.07598 (2024) - [i32]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 abs/2402.08609 (2024) - [i31]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 abs/2402.18762 (2024) - [i30]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 abs/2406.02035 (2024) - [i29]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 abs/2406.02596 (2024) - [i28]Mohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam Mahmood:
Weight Clipping for Deep Continual and Reinforcement Learning. CoRR abs/2407.01704 (2024) - [i27]Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado van Hasselt, Razvan Pascanu, Will Dabney:
Normalization and effective learning rates in reinforcement learning. CoRR abs/2407.01800 (2024) - [i26]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 abs/2407.06756 (2024) - 2023
- [c17]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: 23170-23189 - [c16]Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney:
Understanding Plasticity in Neural Networks. ICML 2023: 23190-23211 - [c15]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: 24517-24531 - [c14]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: 29210-29231 - [c13]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: 33632-33656 - [c12]Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto:
Deep Reinforcement Learning with Plasticity Injection. NeurIPS 2023 - [i25]Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney:
Understanding plasticity in neural networks. CoRR abs/2303.01486 (2023) - [i24]Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto:
Deep Reinforcement Learning with Plasticity Injection. CoRR abs/2305.15555 (2023) - [i23]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 abs/2305.18388 (2023) - [i22]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 abs/2312.04064 (2023) - [i21]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 abs/2312.09187 (2023) - 2022
- [c11]Clare Lyle, Mark Rowland, Will Dabney:
Understanding and Preventing Capacity Loss in Reinforcement Learning. ICLR 2022 - [c10]Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal:
Learning Dynamics and Generalization in Deep Reinforcement Learning. ICML 2022: 14560-14581 - [i20]Clare Lyle, Mark Rowland, Will Dabney:
Understanding and Preventing Capacity Loss in Reinforcement Learning. CoRR abs/2204.09560 (2022) - [i19]Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal:
Learning Dynamics and Generalization in Reinforcement Learning. CoRR abs/2206.02126 (2022) - [i18]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 abs/2212.03319 (2022) - [i17]Clare Lyle:
Generalization Through the Lens of Learning Dynamics. CoRR abs/2212.05377 (2022) - 2021
- [c9]Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney:
On the Effect of Auxiliary Tasks on Representation Dynamics. AISTATS 2021: 1-9 - [c8]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: 3305-3317 - [c7]Benjie Wang
, Clare Lyle, Marta Kwiatkowska:
Provable Guarantees on the Robustness of Decision Rules to Causal Interventions. IJCAI 2021: 4258-4265 - [c6]Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal:
Speedy Performance Estimation for Neural Architecture Search. NeurIPS 2021: 4079-4092 - [c5]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: 28742-28756 - [i16]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 abs/2102.12560 (2021) - [i15]Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney:
On The Effect of Auxiliary Tasks on Representation Dynamics. CoRR abs/2102.13089 (2021) - [i14]Lorenz Kuhn, Clare Lyle, Aidan N. Gomez, Jonas Rothfuss, Yarin Gal:
Robustness to Pruning Predicts Generalization in Deep Neural Networks. CoRR abs/2103.06002 (2021) - [i13]Benjie Wang, Clare Lyle, Marta Kwiatkowska:
Provable Guarantees on the Robustness of Decision Rules to Causal Interventions. CoRR abs/2105.09108 (2021) - [i12]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 abs/2106.02584 (2021) - [i11]Miroslav Fil, Binxin Ru, Clare Lyle, Yarin Gal:
DARTS without a Validation Set: Optimizing the Marginal Likelihood. CoRR abs/2112.13023 (2021) - 2020
- [c4]Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup:
Invariant Causal Prediction for Block MDPs. ICML 2020: 11214-11224 - [c3]Clare Lyle, Lisa Schut, Robin Ru, Yarin Gal, Mark van der Wilk:
A Bayesian Perspective on Training Speed and Model Selection. NeurIPS 2020 - [i10]Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup:
Invariant Causal Prediction for Block MDPs. CoRR abs/2003.06016 (2020) - [i9]Andreas Kirsch, Clare Lyle, Yarin Gal:
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning. CoRR abs/2003.12537 (2020) - [i8]Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal, Benjamin Bloem-Reddy:
On the Benefits of Invariance in Neural Networks. CoRR abs/2005.00178 (2020) - [i7]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 abs/2006.04492 (2020) - [i6]Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk:
A Bayesian Perspective on Training Speed and Model Selection. CoRR abs/2010.14499 (2020)
2010 – 2019
- 2019
- [c2]Clare Lyle, Marc G. Bellemare, Pablo Samuel Castro:
A Comparative Analysis of Expected and Distributional Reinforcement Learning. AAAI 2019: 4504-4511 - [c1]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: 4360-4371 - [i5]Clare Lyle, Pablo Samuel Castro, Marc G. Bellemare:
A Comparative Analysis of Expected and Distributional Reinforcement Learning. CoRR abs/1901.11084 (2019) - [i4]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 abs/1901.11530 (2019) - 2018
- [i3]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 abs/1802.07228 (2018) - [i2]Thang Doan, Bogdan Mazoure, Clare Lyle:
GAN Q-learning. CoRR abs/1805.04874 (2018) - 2016
- [i1]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 abs/1612.04861 (2016)
Coauthor Index
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last updated on 2024-12-02 21:29 CET by the dblp team
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