Ohad Shamir

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Shai Shalev-ShwartzThe Hebrew UniversityVerified email at cs.huji.ac.il
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteVerified email at ttic.edu
Nathan SrebroProfessor, TTIC and University of ChicagoVerified email at ttic.edu
Ofer DekelMicrosoft ResearchVerified email at microsoft.com
Gilad YehudaiPostdoctoral Associate, New York UniversityVerified email at weizmann.ac.il
Nicolò Cesa-BianchiProfessor of Computer Science, Università degli Studi di Milano and Politecnico di MilanoVerified email at unimi.it
Itay SafranAssistant Professor of Computer Science, Ben-Gurion UniversityVerified email at bgu.ac.il
Alexander RakhlinProfessor, MITVerified email at mit.edu
Gal VardiWeizmann Institute of ScienceVerified email at weizmann.ac.il
Shie MannorProfessor of Electrical Engineering @ Technion & Researcher @ NvidiaVerified email at technion.ac.il
Guy KornowskiPhD student, Weizmann Institute of ScienceVerified email at weizmann.ac.il
Ronen EldanWeizmann InstituteVerified email at weizmann.ac.il
Adam Tauman KalaiOpenAIVerified email at openai.com
Tong ZhangUIUCVerified email at tongzhang-ml.org
Blake WoodworthGeorge Washington UniversityVerified email at gwu.edu
Roi LivniTel Aviv UniversityVerified email at tauex.tau.ac.il
Liran SzlakWeizmann Institute of ScienceVerified email at weizmann.ac.il
Eran MalachKempner Institute, HarvardVerified email at fas.harvard.edu
Sham M KakadeHarvard UniversityVerified email at seas.harvard.edu
Sivan SabatoMcMaster University; Canada CIFAR AI Chair, Vector InstituteVerified email at mcmaster.ca

Ohad Shamir

Weizmann Institute of Science

Verified email at weizmann.ac.il - Homepage

Machine Learning Learning Theory


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
The power of depth for feedforward neural networks R Eldan, O Shamir Conference on learning theory, 907-940, 2016	1038	2016
Learnability, stability and uniform convergence S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan The Journal of Machine Learning Research 9999, 2635-2670, 2010	941*	2010
Making gradient descent optimal for strongly convex stochastic optimization A Rakhlin, O Shamir, K Sridharan arXiv preprint arXiv:1109.5647, 2011	860	2011
Optimal Distributed Online Prediction Using Mini-Batches. O Dekel, R Gilad-Bachrach, O Shamir, L Xiao Journal of Machine Learning Research 13 (1), 2012	832	2012
Communication-efficient distributed optimization using an approximate newton-type method O Shamir, N Srebro, T Zhang International conference on machine learning, 1000-1008, 2014	713	2014
Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes O Shamir, T Zhang International conference on machine learning, 71-79, 2013	686	2013
On the computational efficiency of training neural networks R Livni, S Shalev-Shwartz, O Shamir Advances in neural information processing systems 27, 2014	630	2014
Size-independent sample complexity of neural networks N Golowich, A Rakhlin, O Shamir Conference On Learning Theory, 297-299, 2018	505	2018
Better mini-batch algorithms via accelerated gradient methods A Cotter, O Shamir, N Srebro, K Sridharan Advances in neural information processing systems 24, 2011	402	2011
Adaptively learning the crowd kernel O Tamuz, C Liu, S Belongie, O Shamir, AT Kalai arXiv preprint arXiv:1105.1033, 2011	336	2011
Nonstochastic multi-armed bandits with graph-structured feedback N Alon, N Cesa-Bianchi, C Gentile, S Mannor, Y Mansour, O Shamir SIAM Journal on Computing 46 (6), 1785-1826, 2017	322*	2017
Proving the lottery ticket hypothesis: Pruning is all you need E Malach, G Yehudai, S Shalev-Schwartz, O Shamir International Conference on Machine Learning, 6682-6691, 2020	316	2020
Spurious local minima are common in two-layer relu neural networks I Safran, O Shamir International conference on machine learning, 4433-4441, 2018	316	2018
Is local SGD better than minibatch SGD? B Woodworth, KK Patel, S Stich, Z Dai, B Bullins, B Mcmahan, O Shamir, ... International Conference on Machine Learning, 10334-10343, 2020	309	2020
An optimal algorithm for bandit and zero-order convex optimization with two-point feedback O Shamir Journal of Machine Learning Research 18 (52), 1-11, 2017	297	2017
Learning and generalization with the information bottleneck O Shamir, S Sabato, N Tishby Theoretical Computer Science 411 (29-30), 2696-2711, 2010	263	2010
Depth-width tradeoffs in approximating natural functions with neural networks I Safran, O Shamir International conference on machine learning, 2979-2987, 2017	240*	2017
Communication complexity of distributed convex learning and optimization Y Arjevani, O Shamir Advances in neural information processing systems 28, 2015	240	2015
Failures of gradient-based deep learning S Shalev-Shwartz, O Shamir, S Shammah International Conference on Machine Learning, 3067-3075, 2017	236	2017
On the complexity of bandit and derivative-free stochastic convex optimization O Shamir Conference on Learning Theory, 3-24, 2013	230	2013

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