38 citations · 55 across the 8 of their papers we have counts for
8 papers
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks
Yunzhen Feng, Tim G. J. Rudner, Nikolaos Tsilivis +1
Adversarial examples have been shown to cause neural networks to fail on a wide range of vision and language tasks, but recent work has claimed that Bayesian neural networks (BNNs)…
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors
Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson +1
Machine learning models often perform poorly under subpopulation shifts in the data distribution. Developing methods that allow machine learning models to better generalize to such…
Continual Learning via Sequential Function-Space Variational Inference
Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng +2
Sequential Bayesian inference over predictive functions is a natural framework for continual learning from streams of data. However, applying it to neural networks has proved chall…
Tractable Function-Space Variational Inference in Bayesian Neural Networks
Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh +1
Reliable predictive uncertainty estimation plays an important role in enabling the deployment of neural networks to safety-critical settings. A popular approach for estimating the…
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?
Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister +2
Causal confusion is a phenomenon where an agent learns a policy that reflects imperfect spurious correlations in the data. Such a policy may falsely appear to be optimal during tra…
Function-Space Regularization in Neural Networks: A Probabilistic Perspective
Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu +1
Parameter-space regularization in neural network optimization is a fundamental tool for improving generalization. However, standard parameter-space regularization methods make it c…