EPrints submitted by Shai Shalev-Shwartz

Number of EPrints submitted by this user: 57

Online and Batch Learning of Pseudo-Metrics
Shai Shalev-Shwartz, Andrew Y. Ng and Yoram Singer
In: ICML 2004, Banff, Canada(2004).

Learning to Align Polyphonic Music
Shai Shalev-Shwartz, Joseph Keshet and Yoram Singer
In: ISMIR 2004, October 10-14, 2004, Barcelona, Spain.

The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees
Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer
In: NIPS 2004, December 13-16, 2004, Vancouver and Whistler, British Columbia, Canada.

Online and Batch Learning of Pseudo-Metrics
Shai Shalev-Shwartz, Andrew Y. Ng and Yoram Singer
In: ICML 2004, Banff, Canada(2004).

Some Impossibility Results for Budgeted Learning
Nicolò Cesa-Bianchi, Ohad Shamir and Shai Shalev-Shwartz
In: Budgeted Learning Workshop, ICML-COLT 2010(2010).

Smooth $\eps$-Insensitive Regression by Loss ymmetrization
Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer
Journal of Machine Learning Research Volume 6, Number May, pp. 711-741, 2004.

Online Passive-Aggressive Algorithms
Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz and Yoram Singer
Technical Report, Leibniz Center 2005.

The Forgetron: A Kernel-Based Perceptron on a Fixed Budget
Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer
Technical Report, Leibniz Center 2005.

Phoneme Alignment Based on Discriminative Learning
Joseph Keshet, Shai Shalev-Shwartz and Yoram Singer
In: Interspeecj 2005, 4-8 Sep 2005, Lisbon.

A New Perspective on an Old Perceptron Algorithm
Shai Shalev-Shwartz and Yoram Singer
In: COLT 2005, June 27-30, 2005, Bertinoro, Italy.

Learning Preferences Graphs by Soft Projections onto Polyhedra
Shai Shalev-Shwartz and Yoram Singer
Submitted to JLMR 2005.

Online Learning meets Optimization in the Dual
Shai Shalev-Shwartz and Yoram Singer
In: COLT 2006, June 22-25, 2006, Pittsburgh, Pennsylvania, USA.

Online Multiclass Learning by Interclass Hypothesis Sharing
Michael Fink, Shai Shalev-Shwartz, Yoram Singer and Shimon Ullman
In: ICML 2006, June 25-29, 2006, Pittsburgh, Pennsylvania..

The Forgetron: A Kernel-Based Perceptron on a Fixed Budget
Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer
In: NIPS 2005, December 5, 2005, Vancouver, CA.

Online Passive-Aggressive Algorithms
Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz and Yoram Singer
Journal of Machine Learning Research Volume 7, pp. 551-585, 2006.

Convex Repeated Games and Fenchel Duality
Shai Shalev-Shwartz and Yoram Singer
In: NIPS 2006, 4-10 Dec 2006, Vancouver, CA.

Online Classification for Complex Problems Using Simultaneous Projections
Yonatan Amit, Shai Shalev-Shwartz and Yoram Singer
In: NIPS 2006(2006).

Convex Repeated Games and Fenchel Duality
Shai Shalev-Shwartz and Yoram Singer
In: NIPS 2006, 5-10 Dec 2006, Vancouver, CA.

A Primal-Dual Perspective of Online Learning Algorithms
Shai Shalev-Shwartz and Yoram Singer
Machine Learning Journal Volume 69, Number 2, pp. 115-142, 2007. ISSN 1573-0565

Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Shai Shalev-Shwartz, Yoram Singer and Nathan Srebro
In: ICML 2007(2007).

Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking
Shai Shalev-Shwartz and Sivan Sabato
In: COLT 2007(2007).

A Unified Algorithmic Approach for Efficient Online Label Ranking
Shai Shalev-Shwartz and Yoram Singer
In: AISTAT 2007(2007).

Online Classification for Complex Problems Using Simultaneous Projections
Yonatan Amit, Shai Shalev-Shwartz and Yoram Singer
JMLR 2008.

Online Learning: Theory, Algorithms, and Applications
Shai Shalev-Shwartz
(2007) PhD thesis, Hebrew University.

Individual Sequence Prediction using Memory-efficient Context Trees
Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer
IEEE IT 2009.

Stochastic Methods for $\ell_1$ Regularized Loss Minimization
Shai Shalev-Shwartz and Ambuj Tewari
In: ICML 2009, 15-17 Jun 2009, Montreal.

Stochastic Convex Optimization
Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan and Nathan Srebro
In: COLT 2009, 18-20 Jun 2009, Montreal.

Learnability and Stability in the General Learning Setting
Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan and Nathan Srebro
In: COLT 2009, 18-20 Jun 2009, Montreal.

Agnostic Online Learning
Shai Ben-David, David Pal and Shai Shalev-Shwartz
In: COLT 2009, 18-20 Jun 2009, Montreal.

Mind the duality gap: Logarithmic regret algorithms for online optimization
Sham Kakade and Shai Shalev-Shwartz
In: NIPS 2008, Dec 2008, Vancouver.

Fast Rates for Regularized Objectives
karthik Sridharan, Shai Shalev-Shwartz and Nathan Srebro
In: NIPS 2008, Dec 2008, Vancouver.

Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Shai Shalev-Shwartz, Yoram Singer, Nati Srebro and Andrew Cotter
Mathematical Programming B 2010.

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, Number 2, pp. 141-163, 2010.

Trading Accuracy for Sparsity in Optimization Problems with Sparsity Constraints
Shai Shalev-Shwartz, Nati Srebro and Tong Zhang
Siam Journal on Optimization Volume 20, Number 6, 2010.

Composite Objective Mirror Descent
John Duchi, Shai Shalev-Shwartz, Yoram Singer and Ambuj Tewari
In: COLT 2010(2010).

Online Learning of Noisy Data with Kernels
Ohad Shamir, Nicolò Cesa-Bianchi and Shai Shalev-Shwartz
In: COLT 2010(2010).

Efficient Learning with Partially Observed Attributes
Nicolò Cesa-Bianchi, Ohad Shamir and Shai Shalev-Shwartz
In: ICML 2010(2010).

Learning Kernel-Based Halfspaces with the Zero-One Loss
Shai Shalev-Shwartz, Ohad Shamir and karthik sridharan
In: COLT 2010(2010).

Learning from Noisy Data under Distributional Assumptions
Nicolò Cesa-Bianchi, Ohad Shamir and Shai Shalev-Shwartz
In: Robust Statistical Learning Workshop, NIPS 2010(2011).

Online Learning of Noisy Data
Nicolò Cesa-Bianchi, Ohad Shamir and Shai Shalev-Shwartz
IEEE Transactions on Information Theory 2011.

Efficient Learning with Partially Observed Attributes
Nicolò Cesa-Bianchi, Ohad Shamir and Shai Shalev-Shwartz
JMLR 2012.

Learning Kernel Based Halfspaces with the 0-1 Loss
Shai Shalev-Shwartz, Ohad Shamir and Karthik Sridharan
SIAM Journal on Computing 2011.

Stochastic Methods for l1-regularized Loss Minimization
Shai Shalev-Shwartz and Ambuj Tewari
Journal of Machine Learning Research 2011.

Learnability, Stability and Uniform Convergence
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro and Karthik Sridharan
Journal of Machine Learning Research 2010.

ShareBoost: Efficient multiclass learning with feature sharing
Shai Shalev-Shwartz, Yonatan Wexler and Amnon Shashua
In: NIPS 2011, Dec 2011, Granada, Spain.

Large-Scale Convex Minimization with a Low-Rank Constraint
Shai Shalev-Shwartz, Alon Gonen and Ohad Shamir
In: ICML 2011, June 2011, Bellevue, Washington, USA.

Access to Unlabeled Data can Speed up Prediction Time
Ruth Urner, Shai Ben-David and Shai Shalev-Shwartz
In: ICML 2011, June 2011, Bellevue, Washington, USA.

Multiclass Learnability and the ERM principle
Amit Daniely, Sivan Sabato, Shai Ben-David and Shai Shalev-Shwartz
In: COLT 2011, June 2011, Budapest, Hungary.

Collaborative Filtering with the Trace Norm: Learning, Bounding, and Transducing
Ohad Shamir and Shai Shalev-Shwartz
In: COLT 2011, June 2011, Budapest, Hungary.

Quantity Makes Quality: Learning with Information Constraints
Nicolò Cesa-Bianchi, Ohad Shamir and Shai Shalev-Shwartz
In: AAAI 2011(2011).

Using More Data to Speed-up Training Time
Shai Shalev-Shwartz, Ohad Shamir and Eran Tromer
In: AISTATS(2012).

The Kernelized Stochastic Batch Perceptron
Andrew Cotter, Shai Shalev-Shwartz and Nati Srebro
In: ICML(2012).

Near-Optimal Algorithms for Online Matrix Prediction
Elad Hazan, Satyen Kale and Shai Shalev-Shwartz
In: COLT(2012).

Learning Halfspaces with the Zero-One Loss: Time-Accuracy Tradeoffs
Shai Shalev-Shwartz and Aharon Birnbaum
In: NIPS(2012).

Multiclass Learning Approaches: A Theoretical Comparison with Implications
Amit Daniely, Sivan Sabato and Shai Shalev-Shwartz
In: NIPS(2012).

Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
Shai Shalev-Shwartz and Tong Zhang
JMLR 2013.