Clare Lyle
Clare Lyle
University of Oxford
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
A geometric perspective on optimal representations for reinforcement learning
MG Bellemare, W Dabney, R Dadashi, AA Taiga, PS Castro, NL Roux, ...
NeurIPS 2019, 2019
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 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
On The Effect of Auxiliary Tasks on Representation Dynamics
C Lyle, M Rowland, G Ostrovski, W Dabney
AISTATS 2021, 2021
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 Speedy Performance Estimator for Neural Architecture Search
B Ru, C Lyle, L Schut, M van der Wilk, Y Gal
NeurIPS 2021, 2020
Understanding and preventing capacity loss in reinforcement learning
C Lyle, M Rowland, W Dabney
arXiv preprint arXiv:2204.09560, 2022
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
Unpacking information bottlenecks: Unifying information-theoretic objectives in deep learning
A Kirsch, C Lyle, Y Gal
arXiv preprint arXiv:2003.12537, 2020
Gan q-learning
T Doan, B Mazoure, C Lyle
arXiv preprint arXiv:1805.04874, 2018
The Malicious use of artificial intelligence: forecasting, prevention, and mitigation authors are listed in order of contribution design direction
S Bhatnagar, T Cotton, M Brundage, S Avin, J Clark, H Toner, P Eckersley, ...
arXiv Prepr. arXiv1802 7228, 101, 2018
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
Provable Guarantees on the Robustness of Decision Rules to Causal Interventions
B Wang, C Lyle, M Kwiatkowska
IJCAI 2021, 2021
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
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
Understanding plasticity in neural networks
C Lyle, Z Zheng, E Nikishin, BA Pires, R Pascanu, W Dabney
arXiv preprint arXiv:2303.01486, 2023
Understanding Self-Predictive Learning for Reinforcement Learning
Y Tang, ZD Guo, PH Richemond, BÁ Pires, Y Chandak, R Munos, ...
arXiv preprint arXiv:2212.03319, 2022
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