Clare Lyle
Clare Lyle
Google DeepMind
Verified email at - Homepage
Cited by
Cited by
The malicious use of artificial intelligence: Forecasting, prevention, and mitigation
M Brundage, S Avin, J Clark, H Toner, P Eckersley, B Garfinkel, A Dafoe, ...
arXiv preprint arXiv:1802.07228, 2018
Invariant causal prediction for block mdps
A Zhang, C Lyle, S Sodhani, A Filos, M Kwiatkowska, J Pineau, Y Gal, ...
International Conference on Machine Learning, 11214-11224, 2020
A geometric perspective on optimal representations for reinforcement learning
MG Bellemare, W Dabney, R Dadashi, AA Taiga, PS Castro, NL Roux, ...
NeurIPS 2019, 2019
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
J Kossen, N Band, C Lyle, AN Gomez, T Rainforth, Y Gal
NeurIPS 2021, 2021
A comparative analysis of expected and distributional reinforcement learning
C Lyle, MG Bellemare, PS Castro
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4504-4511, 2019
On the benefits of invariance in neural networks
C Lyle, M van der Wilk, M Kwiatkowska, Y Gal, B Bloem-Reddy
arXiv preprint arXiv:2005.00178, 2020
Understanding and preventing capacity loss in reinforcement learning
C Lyle, M Rowland, W Dabney
arXiv preprint arXiv:2204.09560, 2022
On The Effect of Auxiliary Tasks on Representation Dynamics
C Lyle, M Rowland, G Ostrovski, W Dabney
AISTATS 2021, 2021
A Speedy Performance Estimator for Neural Architecture Search
B Ru, C Lyle, L Schut, M van der Wilk, Y Gal
NeurIPS 2021, 2020
Understanding plasticity in neural networks
C Lyle, Z Zheng, E Nikishin, BA Pires, R Pascanu, W Dabney
International Conference on Machine Learning, 23190-23211, 2023
A Bayesian Perspective on Training Speed and Model Selection
C Lyle, L Schut, B Ru, Y Gal, M van der Wilk
Proceedings of the 33rd International Conference on Neural Information …, 2020
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
A Filos, C Lyle, Y Gal, S Levine, N Jaques, G Farquhar
ICML 2021, 2021
Understanding self-predictive learning for reinforcement learning
Y Tang, ZD Guo, PH Richemond, BA Pires, Y Chandak, R Munos, ...
International Conference on Machine Learning, 33632-33656, 2023
Learning dynamics and generalization in deep reinforcement learning
C Lyle, M Rowland, W Dabney, M Kwiatkowska, Y Gal
International Conference on Machine Learning, 14560-14581, 2022
Deep reinforcement learning with plasticity injection
E Nikishin, J Oh, G Ostrovski, C Lyle, R Pascanu, W Dabney, A Barreto
Advances in Neural Information Processing Systems 36, 2024
Gan q-learning
T Doan, B Mazoure, C Lyle
arXiv preprint arXiv:1805.04874, 2018
Unpacking information bottlenecks: Unifying information-theoretic objectives in deep learning
A Kirsch, C Lyle, Y Gal
arXiv preprint arXiv:2003.12537, 2020
Robustness to pruning predicts generalization in deep neural networks
L Kuhn, C Lyle, AN Gomez, J Rothfuss, Y Gal
arXiv preprint arXiv:2103.06002, 2021
Resolving causal confusion in reinforcement learning via robust exploration
C Lyle, A Zhang, M Jiang, J Pineau, Y Gal
Self-Supervision for Reinforcement Learning Workshop-ICLR 2021, 2021
Disentangling the Causes of Plasticity Loss in Neural Networks
C Lyle, Z Zheng, K Khetarpal, H van Hasselt, R Pascanu, J Martens, ...
arXiv preprint arXiv:2402.18762, 2024
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