| The StarCraft Multi-Agent Challenge M Samvelyan, T Rashid, C Schroeder de Witt, G Farquhar, N Nardelli, ... Proceedings of the 18th International Conference on Autonomous Agents and …, 2019 | 147 | 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 33, 2019 | 48 | 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, ... arXiv preprint arXiv:1912.10481, 2019 | 39* | 2019 |
| VIREL: A Variational Inference Framework for Reinforcement Learning M Fellows, A Mahajan, TGJ Rudner, S Whiteson Advances in Neural Information Processing Systems, 2019 | 20 | 2019 |
| Inter-domain Deep Gaussian Processes TGJ Rudner, D Sejdinovic, Y Gal Proceedings of the 37th International Conference on Machine Learning, 2020 | 7* | 2020 |
| On the Connection between Neural Processes and Gaussian Processes with Deep Kernels TGJ Rudner, V Fortuin, YW Teh, Y Gal Workshop on Bayesian Deep Learning (NeurIPS 2018), 2018 | 5 | 2018 |
| The Natural Neural Tangent Kernel: Neural Network Training Dynamics under Natural Gradient Descent TGJ Rudner, F Wenzel, YW Teh, Y Gal Workshop on Bayesian Deep Learning (NeurIPS 2019), 2019 | 2 | 2019 |
| Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ... arXiv preprint arXiv:2106.04015, 2021 | 1 | 2021 |
| Rethinking Function-Space Variational Inference in Bayesian Neural Networks TGJ Rudner, Z Chen, YW Teh, Y Gal Third Symposium on Advances in Approximate Bayesian Inference, 2021 | 1 | 2021 |
| Continual Learning via Function-Space Variational Inference: A Unifying View TGJ Rudner, FB Smith, Q Feng, YW Teh, Y Gal Workshop on Theory and Foundations of Continual Learning (ICML 2021), 2021 | | 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 38th International Conference on Machine Learning, 2021 | | 2021 |
| Outcome-Driven Reinforcement Learning via Variational Inference TGJ Rudner, VH Pong, R McAllister, Y Gal, S Levine arXiv preprint arXiv:2104.10190, 2021 | | 2021 |
| Key Concepts in AI Safety: An Overview TGJ Rudner, H Toner CSET Issue Brief, 2021 | | 2021 |
| Key Concepts in AI Safety: Interpretability in Machine Learning TGJ Rudner, H Toner CSET Issue Brief, 2021 | | 2021 |
| On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations TGJ Rudner, C Lu, MA Osborne, Y Gal, YW Teh Workshop on Robust and Reliable Machine Learning in the Real World (ICLR 2021), 2021 | | 2021 |
Yarin GalAssociate Professor, University of OxfordVerified email at cs.ox.ac.uk
