Metod Jazbec

I am a PhD student at Amsterdam Machine Learning Lab. I am fortunate to be supervised by Eric Nalisnick, and co-supervised by Dan Zhang and Stephan Mandt.

Previously, I completed my Masters in Data Science at ETH Zurich and gained industry experience working as a data scientist and quantitative engineer. I was also a research intern at Microsoft (Cambridge, UK).

Main topics of interest include: uncertainty quantification, generative modeling, efficient AI.

selected publications

preprint

Generative Uncertainty in Diffusion Models

Metod Jazbec, Eliot Wong-Toi, Guoxuan Xia, Dan Zhang, Eric Nalisnick, and Stephan Mandt

2025

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Diffusion models have recently driven significant breakthroughs in generative modeling. While state-of-the-art models produce high-quality samples on average, individual samples can still be low quality. Detecting such samples without human inspection remains a challenging task. To address this, we propose a Bayesian framework for estimating generative uncertainty of synthetic samples. We outline how to make Bayesian inference practical for large, modern generative models and introduce a new semantic likelihood (evaluated in the latent space of a feature extractor) to address the challenges posed by high-dimensional sample spaces. Through our experiments, we demonstrate that the proposed generative uncertainty effectively identifies poor-quality samples and significantly outperforms existing uncertainty-based methods. Notably, our Bayesian framework can be applied post-hoc to any pretrained diffusion or flow matching model (via the Laplace approximation), and we propose simple yet effective techniques to minimize its computational overhead during sampling.
NeurIPS

Fast yet Safe: Early-Exiting with Risk Control

Metod Jazbec*, Alexander Timans*, Tin Hadži Veljković, Kaspar Sakmann, Dan Zhang, Christian A. Naesseth, and Eric Nalisnick

2024

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Scaling machine learning models significantly improves their performance. However, such gains come at the cost of inference being slow and resource-intensive. Early-exit neural networks (EENNs) offer a promising solution: they accelerate inference by allowing intermediate layers to exit and produce a prediction early. Yet a fundamental issue with EENNs is how to determine when to exit without severely degrading performance. In other words, when is it ’safe’ for an EENN to go ’fast’? To address this issue, we investigate how to adapt frameworks of risk control to EENNs. Risk control offers a distribution-free, post-hoc solution that tunes the EENN’s exiting mechanism so that exits only occur when the output is of sufficient quality. We empirically validate our insights on a range of vision and language tasks, demonstrating that risk control can produce substantial computational savings, all the while preserving user-specified performance goals.