On the impact of publicly available news and information transfer to financial markets. - Metod Jazbec; Barna Pásztor; Felix Faltings; Nino Antulov-Fantulin; Petter N. Kolm (CoRR, 2020)

Scalable Gaussian Process Variational Autoencoders. - Metod Jazbec; Vincent Fortuin; Michael Pearce; Stephan Mandt; Gunnar Rätsch (CoRR, 2020)

Factorized Gaussian Process Variational Autoencoders. - Metod Jazbec; Michael Pearce; Vincent Fortuin (CoRR, 2020)

Scalable Gaussian Process Variational Autoencoders. - Metod Jazbec; Matthew Ashman; Vincent Fortuin; Michael Pearce; Stephan Mandt; Gunnar Rätsch (AISTATS, 2021)

Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity. - Metod Jazbec; James Urquhart Allingham; Dan Zhang; Eric T. Nalisnick (NeurIPS, 2023)

Anytime-Valid Confidence Sequences for Consistent Uncertainty Estimation in Early-Exit Neural Networks. - Metod Jazbec; Patrick Forré; Stephan Mandt; Dan Zhang; Eric T. Nalisnick (CoRR, 2023)

Fast yet Safe: Early-Exiting with Risk Control. - Metod Jazbec; Alexander Timans; Tin Hadzi Veljkovic; Kaspar Sakmann; Dan Zhang; Christian Andersson Naesseth; Eric T. Nalisnick (NeurIPS, 2024)

Early-Exit Neural Networks with Nested Prediction Sets. - Metod Jazbec; Patrick Forré; Stephan Mandt; Dan Zhang; Eric T. Nalisnick (UAI, 2024)

DuoDiff: Accelerating Diffusion Models with a Dual-Backbone Approach. - Daniel Gallo Fernández; Razvan-Andrei Matisan; Alejandro Monroy Muñoz; Ana-Maria Vasilcoiu; Janusz Partyka; Tin Hadzi Veljkovic; Metod Jazbec (CoRR, 2024)

Dynamic Vocabulary Pruning in Early-Exit LLMs. - Jort Vincenti; Karim Abdel Sadek; Joan Velja; Matteo Nulli; Metod Jazbec (CoRR, 2024)

Generative Uncertainty in Diffusion Models. - Metod Jazbec; Eliot Wong-Toi; Guoxuan Xia; Dan Zhang; Eric T. Nalisnick; Stephan Mandt (UAI, 2025)

Monitoring Risks in Test-Time Adaptation. - Mona Schirmer; Metod Jazbec; Christian A. Naesseth; Eric T. Nalisnick (CoRR, 2025)