Hippolyt Ritter

According to our database, Hippolyt Ritter authored at least 10 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

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, George Kesidis

Proceedings of the IEEE International Conference on Acoustics, 2023

2022

Black-box coreset variational inference.

Dionysis Manousakas, Hippolyt Ritter, Theofanis Karaletsos

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

TyXe: Pyro-based Bayesian neural nets for Pytorch.

Hippolyt Ritter, Theofanis Karaletsos

Proceedings of Machine Learning and Systems 2022, 2022

2021

Sparse Uncertainty Representation in Deep Learning with Inducing Weights.

Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Addressing Catastrophic Forgetting in Few-Shot Problems.

Pau Ching Yap, Hippolyt Ritter, David Barber

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Bayesian Online Meta-Learning with Laplace Approximation.

Pau Ching Yap, Hippolyt Ritter, David Barber

CoRR, 2020

2019

Gaussian Mean Field Regularizes by Limiting Learned Information.

Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber

Entropy, 2019

2018

Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting.

Hippolyt Ritter, Aleksandar Botev, David Barber

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Scalable Laplace Approximation for Neural Networks.

Hippolyt Ritter, Aleksandar Botev, David Barber

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Practical Gauss-Newton Optimisation for Deep Learning.

Aleksandar Botev, Hippolyt Ritter, David Barber

Proceedings of the 34th International Conference on Machine Learning, 2017