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Results: 15
Number of items: 15
  • 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., 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
    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
    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
    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
  • Open Access
    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 (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2023/hash/af2d9fb5bcee19ef2dfa70d843520c97-Abstract-Conference.html
  • Open Access
    Nalisnick, E., Smyth, P., & Tran, D. (2023). A Brief Tour of Deep Learning from a Statistical Perspective. Annual Review of Statistics and Its Application, 10, 219-246. https://doi.org/10.1146/ANNUREV-STATISTICS-032921-013738
  • Open Access
    Tailor, D., Khan, M. E., & Nalisnick, E. (2023). Exploiting Inferential Structure in Neural Processes. Proceedings of Machine Learning Research, 216, 2089-2098. https://proceedings.mlr.press/v216/tailor23a.html
  • Open Access
    Antorán, J., Janz, D., Allingham, J. U., Daxberger, E., Barbano, R., Nalisnick, E., & Hernández-Lobato, J. M. (2022). Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. Proceedings of Machine Learning Research, 162, 796-821. https://doi.org/10.48550/arXiv.2206.08900
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