Articles with public access mandates - Tim G. J. RudnerLearn more
Not available anywhere: 1
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
Mandates: German Research Foundation, UK Engineering and Physical Sciences Research …
Available somewhere: 14
Tractable Function-Space Variational Inference in Bayesian Neural Networks
TGJ Rudner, Z Chen, YW Teh, Y Gal
Advances in Neural Information Processing Systems (NeurIPS), 2022
Mandates: UK Engineering and Physical Sciences Research Council
VIREL: A Variational Inference Framework for Reinforcement Learning
M Fellows, A Mahajan, TGJ Rudner, S Whiteson
Advances in Neural Information Processing Systems (NeurIPS), 2019
Mandates: UK Engineering and Physical Sciences Research Council, European Commission
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 (ICML), 2022
Mandates: UK Engineering and Physical Sciences Research Council
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
T Papamarkou, M Skoularidou, K Palla, L Aitchison, J Arbel, D Dunson, ...
Proceedings of the International Conference on Machine Learning (ICML), 2024
Mandates: UK Engineering and Physical Sciences Research Council, Agence Nationale de …
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 (NeurIPS), 2021
Mandates: UK Engineering and Physical Sciences Research Council
Outcome-Driven Reinforcement Learning via Variational Inference
TGJ Rudner, VH Pong, R McAllister, Y Gal, S Levine
Advances in Neural Information Processing Systems (NeurIPS), 2021
Mandates: US National Science Foundation, US Department of Defense, UK Engineering and …
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
Mandates: UK Engineering and Physical Sciences Research Council, European Commission
Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution
Y Wang*, TGJ Rudner*, AG Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2023
Mandates: US National Science Foundation
Function-Space Regularization in Neural Networks: A Probabilistic Perspective
TGJ Rudner, S Kapoor, S Qiu, AG Wilson
Proceedings of the International Conference on Machine Learning (ICML), 2023
Mandates: US National Science Foundation, US National Institutes of Health
Should We Learn Most Likely Functions or Parameters?
S Qiu*, TGJ Rudner*, S Kapoor*, AG Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2023
Mandates: US National Science Foundation
On Sequential Bayesian Inference for Continual Learning
S Kessler, A Cobb, TGJ Rudner, S Zohren, SJ Roberts
Entropy, 2023
Mandates: US Department of Defense, UK Engineering and Physical Sciences Research Council
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?
G Gupta, TGJ Rudner, RT McAllister, A Gaidon, Y Gal
Proceedings of the Conference on Causal Learning and Reasoning (CLeaR), 2023
Mandates: UK Engineering and Physical Sciences Research Council
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 (ICML), 2021
Mandates: UK Engineering and Physical Sciences Research Council
A Neural Tangent Kernel Perspective on Function-Space Regularization in Neural Networks
Z Chen, X Shi, TGJ Rudner, Q Feng, W Zhang, T Zhang
NeurIPS Workshop on Optimization for Machine Learning, 2022
Mandates: UK Engineering and Physical Sciences Research Council
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