CS Theory Online Talks The goal of this website is to help find online talks which could be of interest to students and researchers in CS theory. As many of us are home now and cannot travel, this can help us stay connected. Submit new talk (please first check if the talk is not already…
G. Dziugaite, and D. Roy. (2017)cite arxiv:1703.11008Comment: 14 pages, 1 table, 2 figures. Corresponds with UAI camera ready and supplement. Includes additional references and related experiments.
S. Mei, and A. Montanari. (2019)cite arxiv:1908.05355Comment: We added two sections in version 3. One section provides the precise asymptotics of the training error. The other section describes a Gaussian covariate model, which gives the same asymptotic test error as the random features model.
S. Kamath, A. Orlitsky, D. Pichapati, and A. Suresh. Proceedings of The 28th Conference on Learning Theory, volume 40 of Proceedings of Machine Learning Research, page 1066--1100. Paris, France, PMLR, (03--06 Jul 2015)
S. Chatzis. Proceedings of the 30th International Conference on Machine Learning, volume 28 of Proceedings of Machine Learning Research, page 729--737. Atlanta, Georgia, USA, PMLR, (17--19 Jun 2013)
J. Frankle, G. Dziugaite, D. Roy, and M. Carbin. (2019)cite arxiv:1912.05671Comment: This submission subsumes 1903.01611 ("Stabilizing the Lottery Ticket Hypothesis" and "The Lottery Ticket Hypothesis at Scale").
O. Montasser, S. Hanneke, and N. Srebro. Proceedings of the Thirty-Second Conference on Learning Theory, volume 99 of Proceedings of Machine Learning Research, page 2512--2530. Phoenix, USA, PMLR, (25--28 Jun 2019)
D. Simpson, H. Rue, T. Martins, A. Riebler, and S. Sørbye. (2014)cite arxiv:1403.4630Comment: Major revision of previous version. Includes a beefed up literature review and new desiderata for hierarchical priors. Removes (for space) the Cox proportional hazard model and the section on hyperparameters for Gaussian random fields.
F. Tramèr, J. Behrmann, N. Carlini, N. Papernot, and J. Jacobsen. (2020)cite arxiv:2002.04599Comment: Supersedes the workshop paper "Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness" (arXiv:1903.10484).
A. Foong, Y. Li, J. Hernández-Lobato, and R. Turner. (2019)cite arxiv:1906.11537Comment: Presented at the ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning.