受强制性开放获取政策约束的文章 - Shay Moran了解详情
无法在其他位置公开访问的文章：1 篇
Model theory and agnostic online learning via excellent sets
M Malliaris, S Moran
Transactions of the American Mathematical Society 377 (11), 7753-7776, 2024
强制性开放获取政策： European Commission
可在其他位置公开访问的文章：52 篇
Learnability can be undecidable
S Ben-David, P Hrubeš, S Moran, A Shpilka, A Yehudayoff
Nature Machine Intelligence 1 (1), 44-48, 2019
强制性开放获取政策： US National Science Foundation
Private PAC learning implies finite Littlestone dimension
N Alon, R Livni, M Malliaris, S Moran
Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019
强制性开放获取政策： US National Science Foundation, Federal Ministry of Education and Research …
An equivalence between private classification and online prediction
M Bun, R Livni, S Moran
2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS …, 2020
强制性开放获取政策： US National Science Foundation
Sign rank versus Vapnik-Chervonenkis dimension
N Alon, S Moran, A Yehudayoff
Sbornik: Mathematics 208 (12), 1724, 2017
强制性开放获取政策： US National Science Foundation
Active classification with comparison queries
DM Kane, S Lovett, S Moran, J Zhang
2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS …, 2017
强制性开放获取政策： US National Science Foundation
Limits of private learning with access to public data
N Alon, R Bassily, S Moran
Advances in neural information processing systems 32, 2019
强制性开放获取政策： US National Science Foundation
A theory of universal learning
O Bousquet, S Hanneke, S Moran, R Van Handel, A Yehudayoff
Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing …, 2021
强制性开放获取政策： US National Science Foundation
Private query release assisted by public data
R Bassily, A Cheu, S Moran, A Nikolov, J Ullman, S Wu
International Conference on Machine Learning, 695-703, 2020
强制性开放获取政策： US National Science Foundation, Natural Sciences and Engineering Research …
Adversarial laws of large numbers and optimal regret in online classification
N Alon, O Ben-Eliezer, Y Dagan, S Moran, M Naor, E Yogev
Proceedings of the 53rd annual ACM SIGACT symposium on theory of computing …, 2021
强制性开放获取政策： US National Science Foundation
A theory of PAC learnability of partial concept classes
N Alon, S Hanneke, R Holzman, S Moran
2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS …, 2022
强制性开放获取政策： US National Science Foundation
A characterization of multiclass learnability
N Brukhim, D Carmon, I Dinur, S Moran, A Yehudayoff
2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS …, 2022
强制性开放获取政策： European Commission
Near-optimal linear decision trees for k-SUM and related problems
DM Kane, S Lovett, S Moran
Journal of the ACM (JACM) 66 (3), 1-18, 2019
强制性开放获取政策： US National Science Foundation
Towards a unified information-theoretic framework for generalization
M Haghifam, GK Dziugaite, S Moran, D Roy
Advances in Neural Information Processing Systems 34, 26370-26381, 2021
强制性开放获取政策： Natural Sciences and Engineering Research Council of Canada
Teaching and compressing for low VC-dimension
S Moran, A Shpilka, A Wigderson, A Yehudayoff
A Journey Through Discrete Mathematics: A Tribute to Jiří Matoušek, 633-656, 2017
强制性开放获取政策： US National Science Foundation
Private center points and learning of halfspaces
A Beimel, S Moran, K Nissim, U Stemmer
Conference on Learning Theory, 269-282, 2019
强制性开放获取政策： US National Science Foundation
Private and Online Learnability are Equivalent
N Alon, M Bun, R Livni, M Malliaris, S Moran
ACM Journal of the ACM (JACM), 2022
强制性开放获取政策： US National Science Foundation, Federal Ministry of Education and Research …
Labeled compression schemes for extremal classes
S Moran, MK Warmuth
Algorithmic Learning Theory: 27th International Conference, ALT 2016, Bari …, 2016
强制性开放获取政策： US National Science Foundation
Unlabeled sample compression schemes and corner peelings for ample and maximum classes
J Chalopin, V Chepoi, S Moran, MK Warmuth
Journal of Computer and System Sciences 127, 1-28, 2022
强制性开放获取政策： US National Science Foundation, Agence Nationale de la Recherche
A limitation of the PAC-Bayes framework
R Livni, S Moran
Advances in Neural Information Processing Systems 33, 20543-20553, 2020
强制性开放获取政策： US National Science Foundation
出版信息和资助信息由计算机程序自动确定