Jazbec, Metod

11 publications

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

ICLRW 2025 Generative Uncertainty in Diffusion Models Metod Jazbec, Eliot Wong-Toi, Guoxuan Xia, Dan Zhang, Eric Nalisnick, Stephan Mandt

NeurIPS 2025 Monitoring Risks in Test-Time Adaptation Mona Schirmer, Metod Jazbec, Christian A. Naesseth, Eric Nalisnick

NeurIPSW 2024 DuoDiff: Accelerating Diffusion Models with a Dual-Backbone Approach Daniel Gallo Fernández, Răzvan-Andrei Matișan, Alejandro Monroy Muñoz, Ana Maria Vasilcoiu, Janusz Partyka, Tin Hadži Veljković, Metod Jazbec

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

NeurIPS 2024 Fast yet Safe: Early-Exiting with Risk Control Metod Jazbec, Alexander Timans, Tin Hadži Veljković, Kaspar Sakmann, Dan Zhang, Christian A. Naesseth, Eric Nalisnick

ICMLW 2024 Fast yet Safe: Early-Exiting with Risk Control Metod Jazbec, Alexander Timans, Tin Hadži Veljković, Kaspar Sakmann, Dan Zhang, Christian A. Naesseth, Eric Nalisnick

ICMLW 2024 Fast yet Safe: Early-Exiting with Risk Control Metod Jazbec, Alexander Timans, Tin Hadži Veljković, Kaspar Sakmann, Dan Zhang, Christian A. Naesseth, Eric Nalisnick

NeurIPSW 2024 On Efficient Distillation from LLMs to SLMs Metod Jazbec, Menglin Xia, Ankur Mallick, Daniel Madrigal, Dongge Han, Samuel Kessler, Victor Rühle

NeurIPS 2023 Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity Metod Jazbec, James Allingham, Dan Zhang, Eric Nalisnick

AISTATS 2021 Scalable Gaussian Process Variational Autoencoders Metod Jazbec, Matt Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch