Ohad Shamir, Ph.D - Publications

Affiliations: 
Microsoft Research, Redmond, WA, United States 
Area:
Machine Learning

55 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2015 Shamir O, Shalev-Shwartz S. Matrix completion with the trace norm: Learning, bounding, and transducing Journal of Machine Learning Research. 15: 3401-3423.  1
2015 Fetaya E, Shamir O, Ullman S. Graph approximation and clustering on a budget Journal of Machine Learning Research. 38: 241-249.  1
2014 Shamir O, Srebro N. Distributed stochastic optimization and learning 2014 52nd Annual Allerton Conference On Communication, Control, and Computing, Allerton 2014. 850-857. DOI: 10.1109/ALLERTON.2014.7028543  1
2014 Shamir O, Srebro N, Zhang T. Communication-efficient distributed optimization using an approximate Newton-type method 31st International Conference On Machine Learning, Icml 2014. 3: 2665-2681.  1
2014 Shamir O. Fundamental limits of online and distributed algorithms for statistical learning and estimation Advances in Neural Information Processing Systems. 1: 163-171.  1
2014 Livni R, Shalev-Shwartz S, Shamir O. On the computational efficiency of training neural networks Advances in Neural Information Processing Systems. 1: 855-863.  1
2013 Amir A, Zeisel A, Zuk O, Elgart M, Stern S, Shamir O, Turnbaugh PJ, Soen Y, Shental N. High-resolution microbial community reconstruction by integrating short reads from multiple 16S rRNA regions. Nucleic Acids Research. 41: e205. PMID 24214960 DOI: 10.1093/nar/gkt1070  1
2013 Liu B, Sadeghi F, Tappen M, Shamir O, Liu C. Probabilistic label trees for efficient large scale image classification Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 843-850. DOI: 10.1109/CVPR.2013.114  1
2013 Cesa-Bianchi N, Shamir O. Efficient transductive online learning via randomized rounding Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik. 177-194. DOI: 10.1007/978-3-642-41136-6_16  1
2013 Zuk O, Amir A, Zeisel A, Shamir O, Shental N. Accurate profiling of microbial communities from massively parallel sequencing using convex optimization Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8214: 279-297. DOI: 10.1007/978-3-319-02432-5_31  1
2013 Shamir O. On the complexity of bandit and derivative-free stochastic convex optimization Journal of Machine Learning Research. 30: 3-24.  1
2013 Shamir O, Zhang T. Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes 30th International Conference On Machine Learning, Icml 2013. 71-79.  1
2013 Cesa-Bianchi N, Dekel O, Shamir O. Online learning with switching costs and other adaptive adversaries Advances in Neural Information Processing Systems 1
2013 Anava O, Hazan E, Mannor S, Shamir O. Online learning for time series prediction Journal of Machine Learning Research. 30: 172-184.  1
2013 Rakhlin A, Shamir O, Sridharan K. Localization and adaptation in online learning Journal of Machine Learning Research. 31: 516-526.  1
2012 Rubin J, Shamir O, Tishby N. Trading value and information in MDPs Intelligent Systems Reference Library. 28: 57-74. DOI: 10.1007/978-3-642-24647-0_3  1
2012 Rakhlin A, Shamir O, Sridharan K. Relax and randomize: From value to algorithms Advances in Neural Information Processing Systems. 3: 2141-2149.  1
2012 Rakhlin A, Shamir O, Sridharan K. Making gradient descent optimal for strongly convex stochastic optimization Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 1: 449-456.  1
2012 Avner O, Mannor S, Shamir O. Decoupling exploration and exploitation in multi-armed bandits Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 1: 409-416.  1
2012 Shamir O. Open problem: Is averaging needed for strongly convex stochastic gradient descent? Journal of Machine Learning Research. 23: 47.1-47.3.  1
2012 Dekel O, Gilad-Bachrach R, Shamir O, Xiao L. Optimal distributed online prediction using mini-batches Journal of Machine Learning Research. 13: 165-202.  1
2012 Buchbinder N, Chen S, Naor J, Shamir O. Unified algorithms for online learning and competitive analysis Journal of Machine Learning Research. 23: 5.1-5.18.  1
2012 Shalev-Shwartz S, Shamir O, Tromer E. Using more data to speed-up training time Journal of Machine Learning Research. 22: 1019-1027.  1
2012 Urner R, Ben-David S, Shamir O. Learning from weak teachers Journal of Machine Learning Research. 22: 1252-1260.  1
2012 Dekel O, Shamir O. There's a hole in my data space: Piecewise predictors for heterogeneous learning problems Journal of Machine Learning Research. 22: 291-298.  1
2011 Shalev-Shwartz S, Shamir O, Sridharan K. Learning linear and kernel predictors with the 0-1 loss function Ijcai International Joint Conference On Artificial Intelligence. 2740-2745. DOI: 10.5591/978-1-57735-516-8/IJCAI11-456  1
2011 Shalev-Shwartz S, Shamir O, Sridharan K. Learning kernel-based halfspaces with the 0-1 loss Siam Journal On Computing. 40: 1623-1646. DOI: 10.1137/100806126  1
2011 Cesa-Bianchi N, Shalev-Shwartz S, Shamir O. Online learning of noisy data Ieee Transactions On Information Theory. 57: 7907-7931. DOI: 10.1109/TIT.2011.2164053  1
2011 Mannor S, Shamir O. From bandits to experts: On the value of side-observations Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011 1
2011 Kakade SM, Kalai AT, Kanade V, Shamir O. Efficient learning of Generalized Linear and Single Index Models with isotonic regression Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011 1
2011 Cotter A, Shamir O, Srebro N, Sridharan K. Better mini-batch algorithms via accelerated gradient methods Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011 1
2011 Shamir O, Tishby N. Spectral clustering on a budget Journal of Machine Learning Research. 15: 661-669.  1
2011 Cesa-Bianchi N, Shamir O. Efficient online learning via randomized rounding Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011 1
2011 Foygel R, Salakhutdinov R, Shamir O, Srebro N. Learning with the weighted trace-norm under arbitrary sampling distributions Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011 1
2011 Cesa-Bianchi N, Shalev-Shwartz S, Shamir O. Quantity makes quality: Learning with partial views Proceedings of the National Conference On Artificial Intelligence. 2: 1547-1550.  1
2011 Shalev-Shwartz S, Gonen A, Shamir O. Large-scale convex minimization with a low-rank constraint Proceedings of the 28th International Conference On Machine Learning, Icml 2011. 329-336.  1
2011 Tamuz O, Liu C, Belongie S, Shamir O, Kalai AT. Adaptively learning the crowd kernel Proceedings of the 28th International Conference On Machine Learning, Icml 2011. 673-680.  1
2011 Cesa-Bianchi N, Shalev-Shwartz S, Shamir O. Efficient learning with partially observed attributes Journal of Machine Learning Research. 12: 2857-2878.  1
2011 Shamir O, Shalev-Shwartz S. Collaborative filtering with the trace norm: Learning, bounding, and transducing Journal of Machine Learning Research. 19: 661-678.  1
2010 Shamir O, Sabato S, Tishby N. Learning and generalization with the information bottleneck Theoretical Computer Science. 411: 2696-2711. DOI: 10.1016/j.tcs.2010.04.006  1
2010 Shamir O, Tishby N. Stability and model selection in k-means clustering Machine Learning. 80: 213-243. DOI: 10.1007/s10994-010-5177-8  1
2010 Dekel O, Shamir O, Xiao L. Learning to classify with missing and corrupted features Machine Learning. 81: 149-178. DOI: 10.1007/s10994-009-5124-8  1
2010 Cesa-Bianchi N, Shwartz SS, Shamir O. Online learning of noisy data with kernels Colt 2010 - the 23rd Conference On Learning Theory. 218-230.  1
2010 Shalev-Shwartz S, Shamir O, Sridharan K. Learning kernel-based halfspaces with the zero-one loss Colt 2010 - the 23rd Conference On Learning Theory. 441-450.  1
2010 Dekel O, Shamir O. Multiclass-multilabel classification with more classes than examples Journal of Machine Learning Research. 9: 137-144.  1
2010 Kakade SM, Shamir O, Sridharan K, Tewari A. Learning exponential families in high-dimensions: Strong convexity and sparsity Journal of Machine Learning Research. 9: 381-388.  1
2010 Shalev-Shwartz S, Shamir O, Srebro N, Sridharan K. Learnability, stability and uniform convergence Journal of Machine Learning Research. 11: 2635-2670.  1
2009 Dekel O, Shamir O. Good learners for evil teachers Proceedings of the 26th International Conference On Machine Learning, Icml 2009. 233-240. DOI: 10.1145/1553374.1553404  1
2009 Shalev-Shwartz S, Shamir O, Srebro N, Sridharan K. Stochastic convex optimization Colt 2009 - the 22nd Conference On Learning Theory 1
2009 Shamir O, Tishby N. On the reliability of clustering stability in the large sample regime - Supplementary material Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1465-1472.  1
2009 Shalev-Shwartz S, Shamir O, Srebro N, Sridharan K. Learnability and stability in the general learning setting Colt 2009 - the 22nd Conference On Learning Theory 1
2009 Dekel O, Shamir O. Vox populi: Collecting high-quality labels from a crowd Colt 2009 - the 22nd Conference On Learning Theory 1
2009 Shalev-Shwartz S, Shamir O, Sridharan K. The complexity of improperly learning large margin halfspaces Colt 2009 - the 22nd Conference On Learning Theory 1
2009 Shamir O, Tishby N. Cluster stability for finite samples Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference 1
2008 Shamir O, Tishby N. Model selection and stability in k-means clustering 21st Annual Conference On Learning Theory, Colt 2008. 367-378.  1
Show low-probability matches.