Practical Gauss-Newton Optimisation for Deep Learning. - Aleksandar Botev; Hippolyt Ritter; David Barber (ICML, 2017)

A Scalable Laplace Approximation for Neural Networks. - Hippolyt Ritter; Aleksandar Botev; David Barber (ICLR (Poster), 2018)

Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting. - Hippolyt Ritter; Aleksandar Botev; David Barber (NeurIPS, 2018)

Gaussian Mean Field Regularizes by Limiting Learned Information. - Julius Kunze; Louis Kirsch; Hippolyt Ritter; David Barber (Entropy, 2019)

Bayesian Online Meta-Learning with Laplace Approximation. - Pau Ching Yap; Hippolyt Ritter; David Barber (CoRR, 2020)

Addressing Catastrophic Forgetting in Few-Shot Problems. - Pau Ching Yap; Hippolyt Ritter; David Barber (ICML, 2021)

Sparse Uncertainty Representation in Deep Learning with Inducing Weights. - Hippolyt Ritter; Martin Kukla; Cheng Zhang 005; Yingzhen Li (NeurIPS, 2021)

TyXe: Pyro-based Bayesian neural nets for Pytorch. - Hippolyt Ritter; Theofanis Karaletsos (MLSys, 2022)

Black-box coreset variational inference. - Dionysis Manousakas; Hippolyt Ritter; Theofanis Karaletsos (NeurIPS, 2022)

Training Set Cleansing of Backdoor Poisoning by Self-Supervised Representation Learning. - Hang Wang; Sahar Karami; Ousmane Dia; Hippolyt Ritter; Ehsan Emamjomeh-Zadeh; Jiahui Chen; Zhen Xiang; David J. Miller 001; George Kesidis (ICASSP, 2023)