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 | 1074 | 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 | 143 | 2020 |
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 | 106 | 2021 |
A geometric perspective on optimal representations for reinforcement learning MG Bellemare, W Dabney, R Dadashi, AA Taiga, PS Castro, NL Roux, ... NeurIPS 2019, 2019 | 104 | 2019 |
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 | 91 | 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 | 84* | 2020 |
Understanding and preventing capacity loss in reinforcement learning C Lyle, M Rowland, W Dabney arXiv preprint arXiv:2204.09560, 2022 | 79 | 2022 |
On The Effect of Auxiliary Tasks on Representation Dynamics C Lyle, M Rowland, G Ostrovski, W Dabney AISTATS 2021, 2021 | 69 | 2021 |
A Speedy Performance Estimator for Neural Architecture Search B Ru, C Lyle, L Schut, M van der Wilk, Y Gal NeurIPS 2021, 2020 | 52 | 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 | 45 | 2023 |
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 | 29 | 2021 |
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 | 29 | 2020 |
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 | 27 | 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 | 22 | 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 | 21 | 2024 |
Gan q-learning T Doan, B Mazoure, C Lyle arXiv preprint arXiv:1805.04874, 2018 | 17 | 2018 |
Unpacking information bottlenecks: Unifying information-theoretic objectives in deep learning A Kirsch, C Lyle, Y Gal arXiv preprint arXiv:2003.12537, 2020 | 15 | 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 | 11 | 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 | 9 | 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 | 8 | 2024 |