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Results: 19
Number of items: 19
  • 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
    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
    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
  • Open Access
    Verma, R., & Nalisnick, E. (2022). Calibrated Learning to Defer with One-vs-All Classifiers. Proceedings of Machine Learning Research, 162, 22184-22202. https://proceedings.mlr.press/v162/verma22c.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
  • Open Access
    Amiri, S., Belloum, A., Nalisnick, E., Klous, S., & Gommans, L. (2022). On the impact of non-IID data on the performance and fairness of differentially private federated learning. In Proceedings, 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop volume: 27-30 June 2022, Baltimore, Maryland (pp. 52-58). (DSN-W; Vol. 2022). IEEE Computer Society. https://doi.org/10.1109/DSN-W54100.2022.00018
  • Open Access
    Nalisnick, E., Gordon, J., & Hernández-Lobato, J. M. (2021). Predictive Complexity Priors. Proceedings of Machine Learning Research, 130, 694-702. http://proceedings.mlr.press/v130/nalisnick21a.html
  • Open Access
    Daxberger, E., Nalisnick, E., Allingham, J. U., Antorán, J., & Hernández-Lobato, J. M. (2021). Bayesian Deep Learning via Subnetwork Inference. Proceedings of Machine Learning Research, 139, 2510-2521. https://proceedings.mlr.press/v139/daxberger21a.html
  • Open Access
    Pinsler, R., Gordon, J., Nalisnick, E., & Hernández-Lobato, J. M. (2020). Bayesian batch active learning as sparse subset approximation. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), 32nd Conference on Neural Information Processing Systems (NeurIPS 2019): Vancouver, Canada, 8-14 December 2019 (Vol. 8, pp. 6327-6338). (Advances in Neural Information Processing Systems; Vol. 32). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2019/hash/84c2d4860a0fc27bcf854c444fb8b400-Abstract.html
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