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Tim G. J. Rudner
Tim G. J. Rudner
Incoming Assistant Professor & Faculty Fellow, New York University
Bestätigte E-Mail-Adresse bei nyu.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
4722019
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
772019
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ...
Technical Report, 2021
442021
VIREL: A Variational Inference Framework for Reinforcement Learning
M Fellows, A Mahajan, TGJ Rudner, S Whiteson
Advances in Neural Information Processing Systems, 2019
372019
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
222022
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
132021
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
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
102021
Outcome-Driven Reinforcement Learning via Variational Inference
TGJ Rudner, VH Pong, R McAllister, Y Gal, S Levine
Advances in Neural Information Processing Systems, 2021
82021
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
72022
Key Concepts in AI Safety: An Overview
TGJ Rudner, H Toner
Georgetown University Center for Security & Emerging Technology Issue Briefs, 2021
62021
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
52022
Key Concepts in AI Safety: Specification in Machine Learning
TGJ Rudner, H Toner
Georgetown University Center for Security & Emerging Technology Issue Briefs, 2021
42021
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: Robustness and Adversarial Examples
TGJ Rudner, H Toner
Georgetown University Center for Security & Emerging Technology Issue Briefs, 2021
32021
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
12021
Key Concepts in AI Safety: Interpretability in Machine Learning
TGJ Rudner, H Toner
Georgetown University Center for Security & Emerging Technology Issue Briefs, 2021
12021
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