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Results: 5
Number of items: 5
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Jazbec, M., Wong-Toi, E., Xia, G., Zhang, D., Nalisnick, E., & Mandt, S. (2025). Generative Uncertainty in Diffusion Models. Proceedings of Machine Learning Research, 286, 1837-1858. https://proceedings.mlr.press/v286/jazbec25a.html -
Schirmer, M., Jazbec, M., Naesseth, C. A., & Nalisnick, E. (2025). Monitoring Risks in Test-Time Adaptation. In D. Belgrave, C. Zhang, H. Lin, R. Pascanu, P. Koniusz, M. Ghassemi, & N. Chen (Eds.), 39th Annual Conference on Neural Information Processing Systems (NeurIPS 2025): 2-7 December 2025, San Diego, California, USA and 30 November-5 December 2025, Mexico City, Mexico (pp. 89783-89816). (Advances in Neural Information Processing Systems; Vol. 38). Neural Information Processing Systems Foundation. https://doi.org/10.52202/085713-2705 -
Jazbec, M., Timans, A., Veljković, T. H., Sakmann, K., Zhang, D., Naesseth, C. A., & Nalisnick, E. (2025). Fast yet Safe: Early-Exiting with Risk Control. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, & C. Zhang (Eds.), 38th Conference on Neural Information Processing Systems (NeurIPS 2024): 10-15 December 2024, Vancouver, Canada (pp. 129825-129854). (Advances in Neural Information Processing Systems; Vol. 37). Neural Information Processing Systems Foundation. https://doi.org/10.52202/079017-4124 -
Jazbec, M., Forré, P., Mandt, S., Zhang, D., & Nalisnick, E. (2024). Early-Exit Neural Networks with Nested Prediction Sets. Proceedings of Machine Learning Research, 244, 1780-1796. https://proceedings.mlr.press/v244/jazbec24a.html -
Jazbec, M., Allingham, J. U., Zhang, D., & Nalisnick, E. (2023). Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), 37th Conference on Neural Information Processing Systems (NeurIPS 2023): 10-16 December 2023, New Orleans, Louisana, USA (pp. 56138-56168). (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://doi.org/10.52202/075280-2448
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