Multiclass Learnability and also the ERM Principle. Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz. Journal of Machine Learning Research, 16(12 ,):2377-2404, 2015.

Learning Sparse Low-Threshold Straight line Classifiers. Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel Hsu, Tong Zhang. Journal of Machine Learning Research, 16(Jul):1275-1304, 2015.

Faster Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization. Shai Shalev-Shwartz and Tong Zhang. Mathematical Programming SERIES A and B (to look). [pdf on arxiv ]

Matrix Completion using the Trace Norm: Learning, Bounding, and Transducing. Ohad Shamir and Shai Shalev-Shwartz. Journal of Machine Learning Research, 15(March):3401-3423, 2014.

Efficient Active Learning of Halfspaces: A Hostile Approach. Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz. Journal of Machine Learning Research, 14(Sep):2583-2615, 2013.

Stochastic Dual Coordinate Ascent Means of Regularized Loss Minimization. Shai Shalev-Shwartz and Tong Zhang. Journal of Machine Learning Research, 14(February):567-599, 2013.

“Regularization Approaches for Learning with Matrices” Sham Kakade, Shai Shalev-Shwartz, Ambuj Tewari. JMLR 13(Jun):1865-1890, 2012. [Journal paper. Technical Report. Slides of the related talk ]

“Online Learning of Noisy Data” Nicolo Cesa-Bianchi, Shai Shalev-Shwartz and Ohad Shamir. To look in IEEE Transactions on Information Theory, 2011. [Paper: pdf ]

“Efficient Learning with Partly Observed Attributes. Nicolo Cesa-Bianchi, Shai Shalev-Shwartz and Ohad Shamir. JMLR 12(March):2857-2878, 2011. [Paper: pdf ]

“Learning Kernel Based Halfspaces using the -1 Loss” Shai Shalev-Shwartz, Karthik Sridharan and Ohad Shamir. SIAM Journal on Computing, 2011 .

DOI: 10.1137/100806126. [Paper: pdf ]

“Learnability, Stability and Uniform Convergence” Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro and Karthik Sridharan. Journal of Machine Learning Research, 11(March):2635-2670, 2010. [Paper: pdf ]

“Buying and selling Precision for Sparsity in Optimization Issues with Sparsity Constraints” Shai Shalev-Shwartz, Tong Zhang, Nati Srebro, Siam Journal on Optimization. Volume 20, Issue 6, pp. 2807-2832 (2010). DOI 10.1137/090759574. [Paper: pdf ]

“Around the Equivalence of Weak Learnability and Straight line Separability: New Relaxations and Efficient Boosting Algorithms” Shai Shalev-Shwartz and Yoram Singer, Machine Learning Journal, Volume 80, Issue 2, Pages 141 – 163 (2010). DOI 10.1007/s10994-010-5173-z. [Paper: pdf ] (Errata )

“Pegasos: Primal Believed sub-GrAdient SOlver for SVM” Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro, Andrew Cotter.” Mathematical Programming, Series B, 127(1):3-30, 2011. [Paper: pdf ]

“Individual Sequence Conjecture using Memory-efficient Context Trees” Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer, IEEE Transactions on Information Theory. Volume 55, Issue 11, Pages 5251-5262, 2009. [Paper: pdf ]

“Ranking Categorical Features Using Generalization Qualities” Sivan Sabato and Shai Shalev-Shwartz, Journal of Machine Learning Research, 2008. [Paper: pdf ]

“Online Learning of Complex Conjecture Problems Using Synchronised Projections” Yonatan Amit, Shai Shalev-Shwartz and Yoram Siner, Journal of Machine Learning Research, 2008.

[Paper: pdf ]

“A Sizable Margin Formula for Speech-to-Phoneme and Music-to-Score Alignment” Frederick Keshet, Shai Shalev-Shwartz, Yoram Singer and Dan Chazan. IEEE Trans. on Audio, Speech and Language Processing. [Paper: pdf ]

“Efficient Learning of Label Ranking by Soft Projections onto Polyhedra” Shai Shalev-Shwartz and Yoram Singer, Journal of Machine Learning Research 7 (This summer), pages 1567-1599, 2006. [Paper: pdf ]

“Online Passive-Aggressive Algorithms” Koby Crammer, Ofer Dekel, Frederick Keshet, Shai Shalev-Shwartz and Yoram Singer, Journal of Machine Learning Research 7, pages 551-585, 2006. [Paper: pdf ]

“Smooth Epsilon-Insensitive Regression by Loss Symmetrization” Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer, Journal of Machine Learning Research (JMLR), 6(May):711–741, 2005 [Paper: pdf ]

Conference Papers