Erik A. Daxberger

According to our database, Erik A. Daxberger authored at least 9 papers between 2017 and 2022.

Collaborative distances:

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

2022

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning.

Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato

Proceedings of the International Conference on Machine Learning, 2022

2021

Bayesian Deep Learning via Subnetwork Inference.

Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato

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

2020

Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference.

Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato

CoRR, 2020

Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining.

Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Mixed-Variable Bayesian Optimization.

Erik A. Daxberger, Anastasia Makarova, Matteo Turchetta, Andreas Krause

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

2019

Embedding models for episodic knowledge graphs.

Yunpu Ma, Volker Tresp, Erik A. Daxberger

J. Web Semant., 2019

Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection.

Erik A. Daxberger, José Miguel Hernández-Lobato

CoRR, 2019

2018

Embedding Models for Episodic Memory.

Yunpu Ma, Volker Tresp, Erik A. Daxberger

CoRR, 2018

2017

Distributed Batch Gaussian Process Optimization.

Erik A. Daxberger, Bryan Kian Hsiang Low

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