Metod Jazbec

I am a 1st year PhD student in Machine Learning at AMLab / Uva-Bosch Delta Lab where I am fortunate to be supervised by Prof. Eric Nalisnick. Previously, I completed my Masters in Data Science at ETH Zurich and gained industry experience working as a data scientist and quantitative engineer.

My research interests include, but are not limited to: probabilistic modeling, safe and reliable ML, (scalable) Bayesian inference, and financial ML.

news

Oct 3, 2022	Starting my PhD at University of Amsterdam
Aug 27, 2022	Leaving Zurich after 4 great years, time flies. Bis bald Züri!

selected publications

AISTATS

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

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Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors. Amortized Gaussian process inference through GPVAEs has led to significant improvements in this regard, but is still inhibited by the intrinsic complexity of exact GP inference. We improve the scalability of these methods through principled sparse inference approaches. We propose a new scalable GPVAE model that outperforms existing approaches in terms of runtime and memory footprint, is easy to implement, and allows for joint end-to-end optimization of all components.