Erik A. Daxberger

Publication Activity (10 Years)

Years Active: 2017-2023
Publications (10 Years): 13

Top Topics

Top Venues

Trans. Mach. Learn. Res.

Publications

Erik A. Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E. Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan
Improving Continual Learning by Accurate Gradient Reconstructions of the Past. Trans. Mach. Learn. Res. 2023 (2023)
Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. CoRR (2022)
Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. ICML (2022)
Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
Bayesian Deep Learning via Subnetwork Inference. ICML (2021)
Erik A. Daxberger, Anastasia Makarova, Matteo Turchetta, Andreas Krause
Mixed-Variable Bayesian Optimization. IJCAI (2020)
Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference. CoRR (2020)
Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. NeurIPS (2020)
Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. CoRR (2020)
Yunpu Ma, Volker Tresp, Erik A. Daxberger
Embedding models for episodic knowledge graphs. J. Web Semant. 59 (2019)
Erik A. Daxberger, Anastasia Makarova, Matteo Turchetta, Andreas Krause
Mixed-Variable Bayesian Optimization. CoRR (2019)
Erik A. Daxberger, José Miguel Hernández-Lobato
Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection. CoRR (2019)
Yunpu Ma, Volker Tresp, Erik A. Daxberger
Embedding Models for Episodic Memory. CoRR (2018)
Erik A. Daxberger, Bryan Kian Hsiang Low
Distributed Batch Gaussian Process Optimization. ICML (2017)