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; et al. (CoRR, 2016)

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; et al. (CoRR, 2018)

GAN Q-learning. - Thang Doan; Bogdan Mazoure; Clare Lyle (CoRR, 2018)

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

Invariant Causal Prediction for Block MDPs. - Amy Zhang 001; Clare Lyle; Shagun Sodhani; Angelos Filos; Marta Kwiatkowska; Joelle Pineau; Yarin Gal; Doina Precup (ICML, 2020)

A Bayesian Perspective on Training Speed and Model Selection. - Clare Lyle; Lisa Schut; Binxin Ru; Yarin Gal; Mark van der Wilk (NeurIPS, 2020)

Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning. - Andreas Kirsch 002; Clare Lyle; 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)

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 Effect of Auxiliary Tasks on Representation Dynamics. - Clare Lyle; Mark Rowland 001; Georg Ostrovski; Will Dabney (AISTATS, 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 (ICML, 2021)

Provable Guarantees on the Robustness of Decision Rules to Causal Interventions. - Benjie Wang 001; Clare Lyle; Marta Kwiatkowska (IJCAI, 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 (NeurIPS, 2021)

Robustness to Pruning Predicts Generalization in Deep Neural Networks. - Lorenz Kuhn; Clare Lyle; Aidan N. Gomez; Jonas Rothfuss; Yarin Gal (CoRR, 2021)

DARTS without a Validation Set: Optimizing the Marginal Likelihood. - Miroslav Fil; Binxin Ru; Clare Lyle; Yarin Gal (CoRR, 2021)

Understanding and Preventing Capacity Loss in Reinforcement Learning. - Clare Lyle; Mark Rowland 001; Will Dabney (ICLR, 2022)

Learning Dynamics and Generalization in Deep Reinforcement Learning. - Clare Lyle; Mark Rowland 001; Will Dabney; Marta Kwiatkowska; Yarin Gal (ICML, 2022)

Learning Dynamics and Generalization in Reinforcement Learning. - Clare Lyle; Mark Rowland 001; Will Dabney; Marta Kwiatkowska; Yarin Gal (CoRR, 2022)

Generalization Through the Lens of Learning Dynamics. - Clare Lyle (CoRR, 2022)

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)

Understanding Plasticity in Neural Networks. - Clare Lyle; Zeyu Zheng; Evgenii Nikishin; Bernardo Ávila Pires; Razvan Pascanu; Will Dabney (ICML, 2023)

Quantile Credit Assignment. - Thomas Mesnard; Wenqi Chen; Alaa Saade; Yunhao Tang; Mark Rowland 001; Theophane Weber; Clare Lyle; Audrunas Gruslys; Michal Valko; Will Dabney; Georg Ostrovski; Eric Moulines; et al. (ICML, 2023)

The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. - Mark Rowland 001; Yunhao Tang; Clare Lyle; Rémi Munos; Marc G. Bellemare; Will Dabney (ICML, 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 001; Mohammad Gheshlaghi Azar; Charline Le Lan; Clare Lyle; András György 001; Shantanu Thakoor; et al. (ICML, 2023)

Deep Reinforcement Learning with Plasticity Injection. - Evgenii Nikishin; Junhyuk Oh; Georg Ostrovski; Clare Lyle; Razvan Pascanu; Will Dabney; André Barreto 001 (NeurIPS, 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; et al. (CoRR, 2023)

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

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

Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. - Mark Rowland 001; Kevin Kevin Li; 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 001; 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 Philip van Hasselt; Razvan Pascanu; Will Dabney (NeurIPS, 2024)

Weight Clipping for Deep Continual and Reinforcement Learning. - Mohamed Elsayed 003; Qingfeng Lan; Clare Lyle; A. Rupam Mahmood (RLJ, 2024)

Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. - Mark Rowland 001; Li Kevin Wenliang; Rémi Munos; Clare Lyle; Yunhao Tang; Will Dabney (CoRR, 2024)

Frequency and Generalisation of Periodic Activation Functions in Reinforcement Learning. - Augustine N. Mavor-Parker; Matthew J. Sargent; Caswell Barry; Lewis D. Griffin; Clare Lyle (CoRR, 2024)

A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. - Khimya Khetarpal; Zhaohan Daniel Guo; Bernardo Ávila Pires; Yunhao Tang; Clare Lyle; Mark Rowland 001; Nicolas Heess; Diana L. Borsa; Arthur Guez; Will Dabney (AISTATS, 2025)

Agency Is Frame-Dependent. - David Abel; André Barreto 001; Michael Bowling; Will Dabney; Shi Dong; Steven Hansen 001; Anna Harutyunyan; Khimya Khetarpal; Clare Lyle; Razvan Pascanu; Georgios Piliouras; Doina Precup; et al. (CoRR, 2025)

Plasticity as the Mirror of Empowerment. - David Abel; Michael Bowling; André Barreto 001; Will Dabney; Shi Dong; Steven Hansen 001; Anna Harutyunyan; Khimya Khetarpal; Clare Lyle; Razvan Pascanu; Georgios Piliouras; Doina Precup; et al. (CoRR, 2025)

Optimizers Qualitatively Alter Solutions And We Should Leverage This. - Razvan Pascanu; Clare Lyle; Ionut-Vlad Modoranu; Naima Elosegui Borras; Dan Alistarh; Petar Velickovic; Sarath Chandar; Soham De; James Martens (CoRR, 2025)

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning. - Rafal Surdej; Michal Bortkiewicz; Alex Lewandowski; Mateusz Ostaszewski; Clare Lyle (CoRR, 2025)

What Can Grokking Teach Us About Learning Under Nonstationarity? - Clare Lyle; Ghada Sokar; Razvan Pascanu; András György 001 (CoRR, 2025)