Metod Jazbec

According to our database, Metod Jazbec authored at least 18 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Efficient and Uncertainty-Aware Diffusion Framework for Offline-to-Online Reinforcement Learning.

Ha Manh Bui, Metod Jazbec, Eric T. Nalisnick, Anqi Liu

CoRR, May, 2026

A Tale of Two Temperatures: Simple, Efficient, and Diverse Sampling from Diffusion Language Models.

Theo X. Olausson, Metod Jazbec, Xi Wang, Armando Solar-Lezama, Christian A. Naesseth, Stephan Mandt, Eric T. Nalisnick

CoRR, April, 2026

2025

Learning Unmasking Policies for Diffusion Language Models.

Metod Jazbec, Theo X. Olausson, Louis Béthune, Pierre Ablin, Michael Kirchhof, João Monteiro, Victor Turrisi, Jason Ramapuram, Marco Cuturi

CoRR, December, 2025

Dynamic Mixture-of-Experts for Visual Autoregressive Model.

Jort Vincenti, Metod Jazbec, Guoxuan Xia

CoRR, October, 2025

Safe and Efficient In-Context Learning via Risk Control.

Andrea Wynn, Metod Jazbec, Charith Peris, Rinat Khaziev, Anqi Liu, Daniel Khashabi, Eric T. Nalisnick

CoRR, October, 2025

Failure Prediction Is a Better Performance Proxy for Early-Exit Networks Than Calibration.

Piotr Kubaty, Filip Szatkowski, Metod Jazbec, Bartosz Wójcik

CoRR, August, 2025

Generative Uncertainty in Diffusion Models.

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

Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2025

Monitoring Risks in Test-Time Adaptation.

Mona Schirmer, Metod Jazbec, Christian Andersson Naesseth, Eric T. Nalisnick

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

2024

Dynamic Vocabulary Pruning in Early-Exit LLMs.

Jort Vincenti, Karim Abdel Sadek, Joan Velja, Matteo Nulli, Metod Jazbec

CoRR, 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

Early-Exit Neural Networks with Nested Prediction Sets.

Metod Jazbec, Patrick Forré, Stephan Mandt, Dan Zhang, Eric T. Nalisnick

Proceedings of the Uncertainty in Artificial Intelligence, 2024

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

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

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

Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity.

Metod Jazbec, James Urquhart Allingham, Dan Zhang, Eric T. Nalisnick

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2021

Scalable Gaussian Process Variational Autoencoders.

Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Factorized Gaussian Process Variational Autoencoders.

Metod Jazbec, Michael Pearce, Vincent Fortuin

CoRR, 2020

Scalable Gaussian Process Variational Autoencoders.

Metod Jazbec, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch

CoRR, 2020

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