| The StarCraft Multi-Agent Challenge M Samvelyan, T Rashid, C Schroeder de Witt, G Farquhar, N Nardelli, ... Proceedings of the International Conference on Autonomous Agents and Multi …, 2019 | 472 | 2019 |
| A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks A Filos, S Farquhar, AN Gomez, TGJ Rudner, Z Kenton, L Smith, ... Technical Report, 2019 | 87* | 2019 |
| MultiNet: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery TGJ Rudner, M Rußwurm, J Fil, R Pelich, B Bischke, V Kopackova, ... Proceedings of the AAAI Conference on Artificial Intelligence, 2019 | 77 | 2019 |
| Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ... Technical Report, 2021 | 44 | 2021 |
| VIREL: A Variational Inference Framework for Reinforcement Learning M Fellows, A Mahajan, TGJ Rudner, S Whiteson Advances in Neural Information Processing Systems, 2019 | 37 | 2019 |
| Plex: Towards reliability using pretrained large model extensions D Tran, J Liu, MW Dusenberry, D Phan, M Collier, J Ren, K Han, Z Wang, ... arXiv preprint arXiv:2207.07411, 2022 | 22 | 2022 |
| Tractable Function-Space Variational Inference in Bayesian Neural Networks TGJ Rudner, Z Chen, YW Teh, Y Gal Symposium on Advances in Approximate Bayesian Inference, 2021 | 19* | 2021 |
| Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks N Band, TGJ Rudner, Q Feng, A Filos, Z Nado, MW Dusenberry, G Jerfel, ... Advances in Neural Information Processing Systems, 2021 | 13 | 2021 |
| On the Connection between Neural Processes and Gaussian Processes with Deep Kernels TGJ Rudner, V Fortuin, YW Teh, Y Gal NeurIPS Workshop on Bayesian Deep Learning, 2018 | 12 | 2018 |
| On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations TGJ Rudner, C Lu, MA Osborne, Y Gal, YW Teh Advances in Neural Information Processing Systems, 2021 | 10 | 2021 |
| Outcome-Driven Reinforcement Learning via Variational Inference TGJ Rudner, VH Pong, R McAllister, Y Gal, S Levine Advances in Neural Information Processing Systems, 2021 | 8 | 2021 |
| Inter-domain Deep Gaussian Processes TGJ Rudner, D Sejdinovic, Y Gal Proceedings of the International Conference on Machine Learning, 2020 | 8* | 2020 |
| Continual Learning via Sequential Function-Space Variational Inference TGJ Rudner, FB Smith, Q Feng, YW Teh, Y Gal Proceedings of the International Conference on Machine Learning, 2022 | 7 | 2022 |
| Key Concepts in AI Safety: An Overview TGJ Rudner, H Toner Georgetown University Center for Security & Emerging Technology Issue Briefs, 2021 | 6 | 2021 |
| Challenges and opportunities in offline reinforcement learning from visual observations C Lu, PJ Ball, TGJ Rudner, J Parker-Holder, MA Osborne, YW Teh arXiv preprint arXiv:2206.04779, 2022 | 5 | 2022 |
| Key Concepts in AI Safety: Specification in Machine Learning TGJ Rudner, H Toner Georgetown University Center for Security & Emerging Technology Issue Briefs, 2021 | 4 | 2021 |
| The Natural Neural Tangent Kernel: Neural Network Training Dynamics under Natural Gradient Descent TGJ Rudner, F Wenzel, YW Teh, Y Gal NeurIPS Workshop on Bayesian Deep Learning, 2019 | 4 | 2019 |
| Key Concepts in AI Safety: Robustness and Adversarial Examples TGJ Rudner, H Toner Georgetown University Center for Security & Emerging Technology Issue Briefs, 2021 | 3 | 2021 |
| On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes TGJ Rudner, O Key, Y Gal, T Rainforth Proceedings of the International Conference on Machine Learning, 2021 | 1 | 2021 |
| Key Concepts in AI Safety: Interpretability in Machine Learning TGJ Rudner, H Toner Georgetown University Center for Security & Emerging Technology Issue Briefs, 2021 | 1 | 2021 |
Yarin GalAssociate Professor, University of OxfordVerified email at cs.ox.ac.uk
