| 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 | 567 | 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 | 55 | 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 | 48 | 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 | 35* | 2020 |
| 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 | 34 | 2020 |
| On The Effect of Auxiliary Tasks on Representation Dynamics C Lyle, M Rowland, G Ostrovski, W Dabney AISTATS 2021, 2021 | 12 | 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 | 12 | 2020 |
| A Speedy Performance Estimator for Neural Architecture Search B Ru, C Lyle, L Schut, M van der Wilk, Y Gal NeurIPS 2021, 2020 | 12 | 2020 |
| Gan q-learning T Doan, B Mazoure, C Lyle arXiv preprint arXiv:1805.04874, 2018 | 12 | 2018 |
| Unpacking information bottlenecks: Unifying information-theoretic objectives in deep learning A Kirsch, C Lyle, Y Gal arXiv preprint arXiv:2003.12537, 2020 | 10 | 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 | 5 | 2021 |
| 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 | 4 | 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 | 3 | 2021 |
| Provable Guarantees on the Robustness of Decision Rules to Causal Interventions B Wang, C Lyle, M Kwiatkowska IJCAI 2021, 2021 | 2 | 2021 |
| Scalable training with information bottleneck objectives A Kirsch, C Lyle, Y Gal International Conference on Machine Learning (ICML): Workshop on Uncertainty …, 2020 | 2 | 2020 |
| Understanding and Preventing Capacity Loss in Reinforcement Learning C Lyle, M Rowland, W Dabney arXiv preprint arXiv:2204.09560, 2022 | 1 | 2022 |
| 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 | 1 | 2021 |
| DARTS without a Validation Set: Optimizing the Marginal Likelihood M Fil, B Ru, C Lyle, Y Gal arXiv preprint arXiv:2112.13023, 2021 | | 2021 |
| Unpacking Information Bottlenecks: Surrogate Objectives for Deep Learning A Kirsch, C Lyle, Y Gal | | 2020 |
| Some Counterexamples for Compatible Triangulations C Barnson, D Chandler, Q Chen, C Chung, A Coccimiglio, S La, L Li, ... arXiv preprint arXiv:1612.04861, 2016 | | 2016 |
Yarin GalAssociate Professor, University of OxfordVerified email at cs.ox.ac.uk
