Clare Lyle

According to our database, Clare Lyle authored at least 32 papers between 2016 and 2024.

Collaborative distances:

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

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

CoRR, 2024

Mixtures of Experts Unlock Parameter Scaling for Deep RL.

Johan S. Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob N. Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro

CoRR, 2024

Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model.

Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney

CoRR, 2024

2023

Vision-Language Models as a Source of Rewards.

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

CoRR, 2023

Deep Reinforcement Learning with Plasticity Injection.

Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto

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

Understanding Self-Predictive Learning for Reinforcement Learning.

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

Proceedings of the International Conference on Machine Learning, 2023

The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation.

Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney

Proceedings of the International Conference on Machine Learning, 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, Rémi Munos

Proceedings of the International Conference on Machine Learning, 2023

Understanding Plasticity in Neural Networks.

Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2023

2022

Generalization Through the Lens of Learning Dynamics.

Clare Lyle

CoRR, 2022

Learning Dynamics and Generalization in Reinforcement Learning.

Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal

CoRR, 2022

Learning Dynamics and Generalization in Deep Reinforcement Learning.

Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal

Proceedings of the International Conference on Machine Learning, 2022

Understanding and Preventing Capacity Loss in Reinforcement Learning.

Clare Lyle, Mark Rowland, Will Dabney

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

DARTS without a Validation Set: Optimizing the Marginal Likelihood.

Miroslav Fil, Binxin Ru, Clare Lyle, Yarin Gal

CoRR, 2021

Robustness to Pruning Predicts Generalization in Deep Neural Networks.

Lorenz Kuhn, Clare Lyle, Aidan N. Gomez, Jonas Rothfuss, Yarin Gal

CoRR, 2021

Speedy Performance Estimation for Neural Architecture Search.

Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal

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

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

Provable Guarantees on the Robustness of Decision Rules to Causal Interventions.

Benjie Wang, Clare Lyle, Marta Kwiatkowska

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 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

Proceedings of the 38th International Conference on Machine Learning, 2021

On the Effect of Auxiliary Tasks on Representation Dynamics.

Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

A Bayesian Perspective on Training Speed and Model Selection.

Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk

CoRR, 2020

Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search.

Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, Yarin Gal

CoRR, 2020

On the Benefits of Invariance in Neural Networks.

Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal, Benjamin Bloem-Reddy

CoRR, 2020

Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning.

Andreas Kirsch, Clare Lyle, Yarin Gal

CoRR, 2020

A Bayesian Perspective on Training Speed and Model Selection.

Clare Lyle, Lisa Schut, Robin Ru, Yarin Gal, Mark van der Wilk

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Invariant Causal Prediction for Block MDPs.

Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup

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

2019

A Geometric Perspective on Optimal Representations for Reinforcement Learning.

Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Comparative Analysis of Expected and Distributional Reinforcement Learning.

Clare Lyle, Marc G. Bellemare, Pablo Samuel Castro

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

GAN Q-learning.

Thang Doan, Bogdan Mazoure, Clare Lyle

CoRR, 2018

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation.

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

CoRR, 2018

2016

Some Counterexamples for Compatible Triangulations.

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

CoRR, 2016