Search results
Results: 15
Number of items: 15
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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 -
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
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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
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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
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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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