Tim G. J. Rudner
Tim G. J. Rudner
PhD Candidate in Computer Science, University of Oxford
Verified email at cs.ox.ac.uk - Homepage
Cited by
Cited by
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
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
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
VIREL: A Variational Inference Framework for Reinforcement Learning
M Fellows, A Mahajan, TGJ Rudner, S Whiteson
Advances in Neural Information Processing Systems, 2019
Inter-domain Deep Gaussian Processes
TGJ Rudner, D Sejdinovic, Y Gal
Proceedings of the 37th International Conference on Machine Learning, 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), Montréal, Canada, 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), Vancouver, Canada, 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
Outcome-Driven Reinforcement Learning via Variational Inference
TGJ Rudner, VH Pong, R McAllister, Y Gal, S Levine
arXiv preprint arXiv:2104.10190, 2021
Key Concepts in AI Safety: An Overview
TGJ Rudner, H Toner
CSET Issue Brief, 2021
Key Concepts in AI Safety: Interpretability in Machine Learning
TGJ Rudner, H Toner
CSET Issue Brief, 2021
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
TGJ Rudner, O Key, Y Gal, T Rainforth
arXiv preprint arXiv:2011.00515, 2020
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