New online learning survey

"Online Learning and Online Convex Optimization". Shai Shalev-Shwartz. Foundations and Trends in Machine Learning, Volume 4, Issue 2, DOI: 10.1561/2200000018. [Paper: pdf ]

Dissertations

"Online Learning: Theory, Algorithms, and Applications" Shai Shalev-Shwartz, The Hebrew University of Jerusalem. PH.d. thesis. July 2007. [Paper (corrected): pdf ]
(I'd like to thank Francesco Orabona for pointing out important corrections to Figures 5.2 and 5.4 and to Matus Telgars for pointing out a typo in the definition of convex functions.)
"Robust Temporal and Spectral Modeling for Query by Melody" Shai Shalev-Shwartz, The Hebrew University of Jerusalem. M.Sc. thesis. Jerusalem 2002 [Paper: pdf ]

Preprints

Active Learning of Halfspaces under Margin Assumptions. Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz.
Near-Optimal Algorithms for Online Matrix Prediction. Elad Hazan, Satyen Kale, Shai Shalev-Shwartz.
The Kernelized Stochastic Batch Perceptron. Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro.
"On the duality of strong convexity and strong smoothness: Learning applications and matrix regularization" Sham Kakade, Shai Shalev-Shwartz, Ambuj Tewari. [Report, Slides of a related talk]

Journal Papers

"Online Learning of Noisy Data" Nicolo Cesa-Bianchi, Shai Shalev-Shwartz and Ohad Shamir. To appear in IEEE Transactions on Information Theory, 2011. [Paper: pdf ]
"Efficient Learning with Partially Observed Attributes. Nicolo Cesa-Bianchi, Shai Shalev-Shwartz and Ohad Shamir. JMLR 12(Oct):2857-2878, 2011. [Paper: pdf ]
"Learning Kernel Based Halfspaces with the 0-1 Loss" Shai Shalev-Shwartz, Karthik Sridharan and Ohad Shamir. SIAM Journal on Computing, 2011. DOI: 10.1137/100806126. [Paper: pdf ]
"Stochastic Methods for l1-regularized Loss Minimization" Shai Shalev-Shwartz and Ambuj Tewari. Journal of Machine Learning Research, 12(Jun):1865-1892, 2011. [Paper: pdf ]
"Learnability, Stability and Uniform Convergence" Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro and Karthik Sridharan. Journal of Machine Learning Research, 11(Oct):2635-2670, 2010. [Paper: pdf ]
"Trading Accuracy for Sparsity in Optimization Problems 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 ]
"On the Equivalence of Weak Learnability and Linear 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 Estimated 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 Prediction 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 Properties" Sivan Sabato and Shai Shalev-Shwartz, Journal of Machine Learning Research, 2008. [Paper: pdf ]
"Online Learning of Complex Prediction Problems Using Simultaneous Projections" Yonatan Amit, Shai Shalev-Shwartz and Yoram Siner, Journal of Machine Learning Research, 2008. [Paper: pdf ]
"The Forgetron: A Kernel-Based Perceptron on a Budget" Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer, SIAM Journal of COMPUTING, Vol. 37, Issue 5, Pages 1342-1372, 2007. [Paper: pdf ]
"A Primal-Dual Perspective of Online Learning Algorithms" Shai Shalev-Shwartz and Yoram Singer, Machine Learning Journal, 69:2/3, pages 115 - 142, 2007. [Paper: pdf ]
"A Large Margin Algorithm for Speech-to-Phoneme and Music-to-Score Alignment" Joseph 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 (July), pages 1567-1599, 2006. [Paper: pdf ]
"Online Passive-Aggressive Algorithms" Koby Crammer, Ofer Dekel, Joseph 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

"Using More Data to Speed-up Training Time" Shai Shalev-Shwartz, Ohad Shamir, Eran Tromer. AISTATS 2012. [Paper pdf]
"ShareBoost: Efficient multiclass learning with feature sharing" Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua. NIPS 2012. [Paper pdf (full version)]
"Large-Scale Convex Minimization with a Low-Rank Constraint" Shai Shalev-Shwartz, Alon Gonen, and Ohad Shamir. ICML, 2011. [Paper pdf (full version)] [Source code and information on how to reproduce the experiments is available here.]
"Access to Unlabeled Data can Speed up Prediction Time" Ruth Urner, Shai Ben-David, Shai Shalev-Shwartz. ICML, 2011. [Paper pdf]
"Multiclass Learnability and the ERM principle" Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz. COLT, 2011. Received best student paper award. [Paper pdf]
"Collaborative Filtering with the Trace Norm: Learning, Bounding, and Transducing" Ohad Shamir and Shai Shalev-Shwartz. COLT, 2011. [Paper pdf]
"Learning Linear and Kernel Predictors with the 0-1 Loss Function" Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan. IJCAI (Best paper track), 2011. [Paper pdf]
"Quantity Makes Quality: Learning with Information Constraints" Nicolo Cesa-Bianchi, Shai Shalev-Shwartz and Ohad Shamir. AAAI (Nectar track), 2011. [Paper pdf]
"Learning from Noisy Data under Distributional Assumptions" Nicolo Cesa-Bianchi, Shai Shalev-Shwartz and Ohad Shamir. Robust Statistical Learning Workshop, NIPS 2010 [Paper, Full version ]
"Learning Kernel-Based Halfspaces with the Zero-One Loss" Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan. COLT, 2010. Received best paper award. [Paper pdf]
"Efficient Learning with Partially Observed Attributes" Nicolo Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir. ICML, 2010. [Paper with all proofs]
"Some Impossibility Results for Budgeted Learning" Nicolo Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir. Budgeted Learning Workshop, ICML-COLT 2010. [Paper]
"Online Learning of Noisy Data with Kernels" Nicolo Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir. COLT, 2010. [Paper pdf]
"Composite Objective Mirror Descent" John Duchi, Shai Shalev-Shwartz, Yoram Singer, Ambuj Tewari. COLT, 2010.[Paper]
"Stochastic Methods for $\ell_1$ Regularized Loss Minimization" Shai Shalev-Shwartz and Ambuj Tewari. ICML, 2009. [Paper pdf] Errata: Section 3.1 contains errors. Please refer to the long version of the paper [JMLR Paper pdf]
"Stochastic Convex Optimization" Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan and Nati Srebro COLT, 2009. [Paper pdf]
"Learnability and Stability in the General Learning Setting" Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan and Nati Srebro COLT, 2009. [Paper pdf]
"Agnostic Online Learning" Shai Ben-David, David Pal and Shai Shalev-Shwartz COLT, 2009. [Paper pdf]
"Mind the duality gap: Logarithmic regret algorithms for online optimization" Sham Kakade and Shai Shalev-Shwartz. NIPS, 2008. [Paper pdf]
"Fast Rates for Regularized Objectives" Karthik Sridharan, Shai Shalev-Shwartz, Nathan Srebro. NIPS, 2008. [Paper pdf]
"SVM Optimization: Inverse Dependence on Training Set Size" Shai Shalev-Shwartz and Nathan Srebro. ICML 2008. Received best paper award [Paper (corrected): pdf ],[Errata]
"On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms" Shai Shalev-Shwartz and Yoram Singer. COLT 2008. [Paper: pdf ][Talk slides]
"Efficient Bandit Algorithms for Online Multiclass Prediction" Ambuj Tewari, Shai Shalev-Shwartz and Sham Kakade. ICML 2008. [Paper: pdf ][Talk slides]
"Efficient Projections onto the $\ell_1$-Ball for Learning in High Dimensions" John Duchi, Shai Shalev-Shwartz, Yoram Singer, and Tushar Chandra. ICML 2008. [Paper: pdf ]
"Pegasos: Primal Estimated sub-GrAdient SOlver for SVM" Shai Shalev-Shwartz, Yoram Singer, and Nathan Srebro. ICML 2007. [Paper: pdf ] [Talk Slides: ppt ] A source code is available here.
A technical report with a generalized logarithmic regret and detailed proofs:
"Logarithmic Regret Algorithms for Strongly Convex Repeated Games" ,Technical Report [2007-42], The Hebrew University, May 2007. [Paper: pdf]
"Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking" Sivan Sabato and Shai Shalev-Shwartz, COLT 2007. [Paper: pdf ]
A version with all the proofs: [Paper: pdf]
"A Unified Algorithmic Approach for Efficient Online Label Ranking" Shai Shalev-Shwartz and Yoram Singer, AISTAT 2007. [Paper: pdf ]
"Convex Repeated Games and Fenchel Duality" Shai Shalev-Shwartz and Yoram Singer, NIPS 2006. [Paper: pdf ]
A version with all the proofs: [Paper: pdf]
"Online Classification for Complex Problems Using Simultaneous Projections" Yonatan Amit, Shai Shalev-Shwartz, and Yoram Singer, NIPS 2006. [Paper: pdf ]
"Online Learning meets Optimization in the Dual" Shai Shalev-Shwartz and Yoram Singer, COLT 2006. [Paper: pdf ]
A version with all the proofs: Technical Report[2006-2], Leibniz Center, 2006. [Paper: pdf]
"Online Multiclass Learning by Interclass Hypothesis Sharing" Michael Fink, Shai Shalev-Shwartz, Yoram Singer and Shimon Ullman ICML 2006. [Paper: pdf ]
"The Forgetron: A Kernel-Based Perceptron on a Fixed Budget." Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer, Advances in Neural Information Processing Systems 17, MIT Press, 2005. Received "Outstanding student paper award". [Paper: pdf ] [A journal version with proofs: pdf ] [Talk Slides: ppt ] [Poster: ppt ]
"Phoneme Alignment Based on Discriminative Learning" Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer, and Dan Chazan. Interspeech 2005 [Paper: pdf ]
"A New Perspective on an Old Perceptron Algorithm" Shai Shalev-Shwartz and Yoram Singer, Proceedings of the Sixteenth Annual Conference on Computational Learning Theory, 2005 [Paper: pdf ] [Errata (thanks to Francesco Orabona for pointing out a mistake in the paper)]
"The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees" Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer, Advances in Neural Information Processing Systems 17, MIT Press, 2004. [Paper: pdf ] [Talk Slides: ppt ]
"Learning to Align Polyphonic Music" Shai Shalev-Shwartz, Joseph Keshet and Yoram Singer. ISMIR 2004 Webpage for the paper [Paper: pdf ] [Long version: pdf ] [Talk Slides: ppt ]
"Online and Batch Learning of Pseudo-Metrics" Shai Shalev-Shwartz, Yoram Singer and Andrew Y. Ng. ICML 2004 [Paper: pdf ] [Talk Slides: ppt ]
"Online Passive-Aggressive Algorithms" Koby Crammer, Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer, Advances in Neural Information Processing Systems 16, MIT Press, 2003. [Paper: pdf ] [Talk Slides: ppt ]
"Smooth Epsilon-Insensitive Regression by Loss Symmetrization" Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer, Proceedings of the Sixteenth Annual Conference on Computational Learning Theory, pages 433-447,Springer LNAI 2777, 2003 [Paper: pdf ] [Talk slides: ppt ]
"Robust Temporal and Spectral Modeling for Query by Melody" Shai Shalev-Shwartz, Shlomo Dubnov, Nir Friedman and Yoram Singer, Proceedings of the 25rd Conference on Research and Development in Information Retrieval (SIGIR), 2002. [Paper: pdf ] [Talk slides: ppt mp3 files (tar) ]

Other publications (Tech Reports, Workshops, Demonstrations, ...)

"Trading Accuracy for Sparsity" Shai Shalev-Shwartz, Nathan Srebro, Tong Zhang. Technical Report TTIC-TR-2009-3, May 2009. [pdf]
"Agnostic Online Learnability" Shai Shalev-Shwartz. Technical Report TTIC-TR-2008-2, October 2008. [Report] A much improved version [appears in COLT], together with Shai Ben-David and David Pal.
"Low \ell_1 Norm and Guarantees on Sparsifiability" Shai Shalev-Shwartz and Nathan Srebro. Sparse Optimization and Variable Selection, Workshop, ICML/COLT/UAI, July, 2008. [Extended abstract, Report] [Talk slides]
"Iterative Loss Minimization with $\ell_1$-Norm Constraint and Guarantees on Sparsity" Shai Shalev-Shwartz and Nathan Srebro. Technical Report, TTI, 2008. [Report]
"A Demonstration of a Query by Melody system" Shai Shalev-Shwartz and Yoram Singer, Presented in NIPS, 2003. [Movie file: mp4 ]