Metod Jazbec

University of Amsterdam, Informatics Institute


I am a PhD student at Amsterdam Machine Learning Lab where I am fortunate to be supervised by Eric Nalisnick, and co-supervised by Dan Zhang (Bosch AI) and Stephan Mandt (UC Irvine).

Previously, I completed my Masters in Data Science at ETH Zurich and gained industry experience working as a data scientist and quantitative engineer.

My main current research goal is to build ML systems that can reason about uncertainty in real time. Other interests include, but are not limited to, probabilistic ML, (scalable) Bayesian inference, and generative modelling.

selected publications

  1. NeurIPS
    Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity
    Metod Jazbec, James Urquhart Allingham, Dan Zhang, and Eric Nalisnick
    Advances in Neural Information Processing Systems (NeurIPS) 2023
    Scalable gaussian process variational autoencoders
    Metod Jazbec, Matt Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, and Gunnar Rätsch
    International Conference on Artificial Intelligence and Statistics (AISTATS) 2021