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Results: 19
Number of items: 19
  • Open Access
    Amiri, S. (2026). From patient to pattern: Probabilistic models for synthetic data in digital health twin analytics. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Timans, A., Verma, R., Nalisnick, E., & Naesseth, C. A. (2025). On Continuous Monitoring of Risk Violations under Unknown Shift. Proceedings of Machine Learning Research, 286, 4204-4215. https://proceedings.mlr.press/v286/timans25a.html
  • Open Access
    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
  • Open Access
    Timans, A., Straehle, C.-N., Sakmann, K., Naesseth, C. A., & Nalisnick, E. (2025). Max-Rank: Efficient Multiple Testing for Conformal Prediction. Proceedings of Machine Learning Research, 258, 3898-3906. https://proceedings.mlr.press/v258/timans25a.html
  • Open Access
    Timans, A., Straehle, C.-N., Sakmann, K., & Nalisnick, E. (2025). Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, & G. Varol (Eds.), Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024 : proceedings (Vol. LXXXVIII, pp. 363–398). (Lecture Notes in Computer Science; Vol. 15146). Springer. https://doi.org/10.1007/978-3-031-73223-2_21
  • Open Access
    Schirmer, M., Zhang, D., & Nalisnick, E. (2025). Temporal Test-Time Adaptation with State-Space Models. Transactions on Machine Learning Research, 2025, Article 5244. https://openreview.net/forum?id=HFETOmUtrV
  • Open Access
    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
  • Open Access
    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
  • Open Access
    Amiri, S., Nalisnick, E., Belloum, A., Klous, S., & Gommans, L. (2024). Practical Modelling of Mixed-Tailed Data with Normalizing Flows. Transactions on Machine Learning Research, 2024(10), Article 2998. https://openreview.net/forum?id=uphsKDj0Uu
  • Open Access
    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
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