| 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 |
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