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Tim G. J. Rudner
Tim G. J. Rudner
Incoming Assistant Professor & Faculty Fellow, New York University
Verified email at nyu.edu - Homepage
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Cited by
Cited by
Year
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
5932019
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
100*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
872019
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ...
Technical Report, 2021
592021
VIREL: A Variational Inference Framework for Reinforcement Learning
M Fellows, A Mahajan, TGJ Rudner, S Whiteson
Advances in Neural Information Processing Systems, 2019
402019
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
352022
Tractable Function-Space Variational Inference in Bayesian Neural Networks
TGJ Rudner, Z Chen, YW Teh, Y Gal
Advances in Neural Information Processing Systems, 2022
21*2022
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
162021
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
152021
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
122022
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
122018
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
112022
Outcome-Driven Reinforcement Learning via Variational Inference
TGJ Rudner, VH Pong, R McAllister, Y Gal, S Levine
Advances in Neural Information Processing Systems, 2021
92021
Key Concepts in AI Safety: An Overview
TGJ Rudner, H Toner
Georgetown University Center for Security & Emerging Technology Issue Briefs, 2021
82021
Inter-domain Deep Gaussian Processes
TGJ Rudner, D Sejdinovic, Y Gal
Proceedings of the International Conference on Machine Learning, 2020
8*2020
Key Concepts in AI Safety: Robustness and Adversarial Examples
TGJ Rudner, H Toner
Georgetown University Center for Security & Emerging Technology Issue Briefs, 2021
62021
Key Concepts in AI Safety: Specification in Machine Learning
TGJ Rudner, H Toner
Georgetown University Center for Security & Emerging Technology Issue Briefs, 2021
52021
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
42019
Key Concepts in AI Safety: Interpretability in Machine Learning
TGJ Rudner, H Toner
Georgetown University Center for Security & Emerging Technology Issue Briefs, 2021
22021
On Sequential Bayesian Inference for Continual Learning
S Kessler, A Cobb, TGJ Rudner, S Zohren, SJ Roberts
Entropy, 2023
12023
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