Hi there, I am Erik, a PhD student in the Machine Learning Group at the University of Cambridge, supervised by José Miguel Hernández-Lobato.
As a Cambridge-Tübingen fellow, I will also spend a year of my PhD at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, at the department led by Bernhard Schölkopf.
My research interests broadly revolve around machine learning and artificial intelligence, with a current focus on methods at the intersection of probabilistic modeling and deep learning.
Before embarking on my PhD, I obtained a Master's degree in Computer Science from ETH Zurich, where I also did research on discrete and mixed-variable Bayesian optimization with Andreas Krause. Prior to that, I was based in my hometown of Munich, Germany, where I obtained a Bachelor's degree in Computer Science from Ludwigs-Maximilians-Universität, and did an internship at Siemens, working on statistical relational learning with Volker Tresp. I also spent a great year at the National University of Singapore, where I did research on batch Bayesian optimization with Bryan Kian Hsiang Low.
|Bayesian Variational Autoencoders for|
Unsupervised Out-of-Distribution Detection
Erik Daxberger, José Miguel Hernández-Lobato
Bayesian Deep Learning Workshop, NeurIPS 2019
|Mixed-Variable Bayesian Optimization|
Erik Daxberger*, Anastasia Makarova*, Matteo Turchetta*, Andreas Krause
|Embedding Models for Episodic Knowledge Graphs|
Yunpu Ma, Volker Tresp, Erik A. Daxberger
Journal of Web Semantics 2018
|Distributed Batch Gaussian Process Optimization
Erik A. Daxberger, Bryan Kian Hsiang Low
[Paper] [Appendix] [Talk]