S. Shalev-Shwartz

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Logarithmic Regret Algorithms for Strongly Convex Repeated Games

Y. Singer, S. Shalev-Shwartz, 2007 .

Low ` 1-Norm and Guarantees on Sparsifiability

S. Shalev-Shwartz, 2008 .

Stochastic methods for l1 regularized loss minimization

Ambuj Tewari, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2009, ICML '09.

Pegasos: primal estimated sub-gradient solver for SVM

Yoram Singer, Nathan Srebro, Andrew Cotter, 2011, Math. Program..

Applications of strong convexity--strong smoothness duality to learning with matrices pdf

Ambuj Tewari, Sham M. Kakade, Shai Shalev-Shwartz, 2009, ArXiv.

On the duality of strong convexity and strong smoothness : Learning applications and matrix regularization

S. Kakade, S. Shalev-Shwartz, 2009 .

SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data pdf

Shai Shalev-Shwartz, Amir Globerson, Eran Malach, 2017, ICLR.

Efficient bandit algorithms for online multiclass prediction

Ambuj Tewari, Sham M. Kakade, Shai Shalev-Shwartz, 2008, ICML '08.

Learnability, Stability and Uniform Convergence

Ohad Shamir, Nathan Srebro, Karthik Sridharan, 2010, J. Mach. Learn. Res..

Stochastic dual coordinate ascent methods for regularized loss

Shai Shalev-Shwartz, Tong Zhang, S. Shalev-Shwartz, 2012, J. Mach. Learn. Res..

Pegasos: primal estimated sub-gradient solver for SVM

Yoram Singer, Nathan Srebro, Shai Shalev-Shwartz, 2007, ICML '07.

Convex Repeated Games and Fenchel Duality

Yoram Singer, Shai Shalev-Shwartz, Y. Singer, 2006, NIPS.

Online Learning Meets Optimization in the Dual

Yoram Singer, Shai Shalev-Shwartz, Y. Singer, 2006, COLT.

Online Passive-Aggressive Algorithms

Koby Crammer, Yoram Singer, Shai Shalev-Shwartz, 2003, J. Mach. Learn. Res..

Composite objective mirror descent

Ambuj Tewari, Yoram Singer, Shai Shalev-Shwartz, 2010, COLT 2010.

Efficient projections onto the l1-ball for learning in high dimensions

Yoram Singer, Shai Shalev-Shwartz, John C. Duchi, 2008, ICML '08.

SVM optimization: inverse dependence on training set size

Nathan Srebro, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2008, ICML '08.

Fast Convergence Rates for Excess Regularized Risk with Application to SVM

S. Shalev-Shwartz, Nathan Srebro, Karthik Sridharan, 2008 .

Stochastic Gradient Descent

S. Shalev-Shwartz, S. Ben-David, 2014 .

Near-Optimal Algorithms for Online Matrix Prediction

Shai Shalev-Shwartz, Elad Hazan, Satyen Kale, 2012, COLT.

Stochastic methods for {\it l}$_{\mbox{1}}$ regularized loss minimization

S. Shalev-Shwartz, Ambuj Tewari, 2009, ICML 2009.

On the equivalence of weak learnability and linear separability: new relaxations and efficient boosting algorithms

Yoram Singer, Shai Shalev-Shwartz, Y. Singer, 2010, Machine Learning.

On the Computational Efficiency of Training Neural Networks

Roi Livni, Ohad Shamir, Shai Shalev-Shwartz, 2014, NIPS.

Large-Scale Convex Minimization with a Low-Rank Constraint pdf

Ohad Shamir, Shai Shalev-Shwartz, Alon Gonen, 2011, ICML.

Fast Rates for Regularized Objectives

Nathan Srebro, Karthik Sridharan, Shai Shalev-Shwartz, 2008, NIPS.

Stochastic Convex Optimization

Ohad Shamir, Nathan Srebro, Karthik Sridharan, 2009, COLT.

Online learning: theory, algorithms and applications (למידה מקוונת.)

Shai Shalev-Shwartz, S. Shalev-Shwartz, Shai Shalev-Shwartz, 2007 .

Online and batch learning of pseudo-metrics

Yoram Singer, Andrew Y. Ng, Shai Shalev-Shwartz, 2004, ICML.

Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization

Tong Zhang, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2013, Math. Program..

The Forgetron: A Kernel-Based Perceptron on a Fixed Budget

Yoram Singer, Shai Shalev-Shwartz, Ofer Dekel, 2005, NIPS.

Proximal Stochastic Dual Coordinate Ascent pdf

Tong Zhang, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2012, ArXiv.

Online Learning and Online Convex Optimization

Shai Shalev-Shwartz, S. Shalev-Shwartz, Shai Shalev-Shwartz, 2012, Found. Trends Mach. Learn..

Understanding Machine Learning: From Theory to Algorithms

Shai Ben-David, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2014 .

Mind the Duality Gap: Logarithmic regret algorithms for online optimization

Sham M. Kakade, Shai Shalev-Shwartz, S. Kakade, 2008, NIPS.

Efficient Learning with Partially Observed Attributes

Ohad Shamir, Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, 2010, ICML.

Beyond Convexity: Stochastic Quasi-Convex Optimization

Shai Shalev-Shwartz, Elad Hazan, Kfir Y. Levy, 2015, NIPS.

From average case complexity to improper learning complexity

Nathan Linial, Amit Daniely, Shai Shalev-Shwartz, 2013, STOC.

Trading Accuracy for Sparsity

S. Shalev-Shwartz, 2009 .

A primal-dual perspective of online learning algorithms

Yoram Singer, Shai Shalev-Shwartz, Y. Singer, 2007, Machine Learning.

The Forgetron: A Kernel-Based Perceptron on a Budget

Yoram Singer, Shai Shalev-Shwartz, Ofer Dekel, 2008, SIAM J. Comput..

Multiclass Learnability and the ERM principle

Shai Ben-David, Amit Daniely, Shai Shalev-Shwartz, 2011, COLT.

Trading Accuracy for Sparsity in Optimization Problems with Sparsity Constraints

Tong Zhang, Nathan Srebro, Shai Shalev-Shwartz, 2010, SIAM J. Optim..

Efficient projections onto the {\it l}$_{\mbox{1}}$-ball for learning in high dimensions

Yoram Singer, Shai Shalev-Shwartz, Tushar Deepak Chandra, 2008, International Conference on Machine Learning.

On Lower and Upper Bounds in Smooth and Strongly Convex Optimization

Ohad Shamir, Shai Shalev-Shwartz, Yossi Arjevani, 2016, J. Mach. Learn. Res..

Learning Kernel-Based Halfspaces with the 0-1 Loss

Ohad Shamir, Karthik Sridharan, Shai Shalev-Shwartz, 2011, SIAM J. Comput..

On Graduated Optimization for Stochastic Non-Convex Problems pdf

Shai Shalev-Shwartz, Elad Hazan, Kfir Yehuda Levy, 2015, ICML.

Smooth e-Intensive Regression by Loss Symmetrization

Yoram Singer, Shai Shalev-Shwartz, Ofer Dekel, 2005, COLT.

Smooth ε-insensitive regression by loss symmetrization

Yoram Singer, Shai Shalev-Shwartz, Ofer Dekel, 2003, COLT 2003.

Learning to Align Polyphonic Music

Yoram Singer, Shai Shalev-Shwartz, Joseph Keshet, 2004, ISMIR.

Regularization Techniques for Learning with Matrices

Ambuj Tewari, Sham M. Kakade, Shai Shalev-Shwartz, 2009, J. Mach. Learn. Res..

On Lower and Upper Bounds for Smooth and Strongly Convex Optimization Problems pdf

Ohad Shamir, Shai Shalev-Shwartz, Yossi Arjevani, 2015, ArXiv.

Individual Sequence Prediction Using Memory-Efficient Context Trees

Yoram Singer, Shai Shalev-Shwartz, Ofer Dekel, 2009, IEEE Transactions on Information Theory.

M ay 2 01 6 SDCA without Duality , Regularization , and Individual Conv exity

S. Shalev-Shwartz, 2018 .

Standing on the Shoulders of Giant Frozen Language Models

S. Shalev-Shwartz, Y. Shoham, A. Shashua, 2022, ArXiv.

Effective Semi-supervised Learning on Manifolds

S. Shalev-Shwartz, A. Globerson, 2017 .

TTIC-TR-2009-3 May 2009 Trading Accuracy for Sparsity

S. Shalev-Shwartz, 2009 .

Smooth epsiloon-Insensitive Regression by Loss Symmetrization

Yoram Singer, Shai Shalev-Shwartz, Ofer Dekel, 2005, Journal of machine learning research.

Agnostically Learning Halfspaces with Margin Errors

Shai Shalev-Shwartz, S. Shalev-Shwartz, 2009 .

Provable Guarantees on Learning Hierarchical Generative Models with Deep CNNs

Shai Shalev-Shwartz, Eran Malach, S. Shalev-Shwartz, 2018 .

Understanding Machine Learning: Preface

Shai Ben-David, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2014 .

The Connection Between Approximation, Depth Separation and Learnability in Neural Networks pdf

Gilad Yehudai, Ohad Shamir, Shai Shalev-Shwartz, 2021, COLT.

The error rate of learning halfspaces using Kernel-SVMs pdf

Nathan Linial, Amit Daniely, Shai Shalev-Shwartz, 2012, ArXiv.

Low ` 1-Norm and Guarantees on Sparsifiability

S. Shalev-Shwartz, 2008 .

Feature Selection and Generation

Shai Ben-David, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2014 .

Understanding Machine Learning: Multiclass Learnability

Shai Ben-David, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2014 .

Understanding Machine Learning: The Bias-Complexity Trade-off

Shai Ben-David, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2014 .

A Formal Learning Model

Shai Ben-David, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2014 .

Effective Semisupervised Learning on Manifolds

Roi Livni, Shai Shalev-Shwartz, Amir Globerson, 2017, COLT.

Multiclass, Ranking, and Complex Prediction Problems

Shai Ben-David, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2014 .

Theory and Practice of Support Vector Machines Optimization

Shai Shalev-Shwartz, Nathan Srebo, S. Shalev-Shwartz, 2009 .

MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning

Ehud D. Karpas, S. Shalev-Shwartz, Y. Shoham, 2022, ArXiv.

The Implicit Bias of Depth: How Incremental Learning Drives Generalization

S. Shalev-Shwartz, Amit Daniely, Daniel Gissin, 2019, ICLR.

Can We Learn to Gamble Efficiently?

Dominik D. Freydenberger, R. Munos, Shie Mannor, 2010, COLT.

Efficient Learning of CNNs using Patch Based Features

S. Shalev-Shwartz, Alon Brutzkus, A. Globerson, 2022, ICML.

Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving pdf

Amnon Shashua, Shai Shalev-Shwartz, Shaked Shammah, 2016, ArXiv.

Long-term Planning by Short-term Prediction pdf

Amnon Shashua, Shai Shalev-Shwartz, Aviad Cohen, 2016, ArXiv.

Solving Ridge Regression using Sketched Preconditioned SVRG pdf

Francesco Orabona, Shai Shalev-Shwartz, Alon Gonen, 2016, ICML.

Huge Frozen Language Models as Readers for Open-Domain Question Answering

S. Shalev-Shwartz, Y. Shoham, A. Shashua, 2022 .

SenseBERT: Driving Some Sense into BERT

Yoav Shoham, Amnon Shashua, Shai Shalev-Shwartz, 2019, ACL.

Discriminative Active Learning pdf

Shai Shalev-Shwartz, Daniel Gissin, S. Shalev-Shwartz, 2019, ArXiv.

Decoupling "when to update" from "how to update"

Shai Shalev-Shwartz, Eran Malach, S. Shalev-Shwartz, 2017, NIPS.

Quantity Makes Quality: Learning with Partial Views

Ohad Shamir, Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, 2011, AAAI.

Access to Unlabeled Data can Speed up Prediction Time

Shai Ben-David, Ruth Urner, Shai Shalev-Shwartz, 2011, ICML.

Complexity Theoretic Limitations on Learning DNF's pdf

Amit Daniely, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2014, COLT.

Ranking Categorical Features Using Generalization Properties

Shai Shalev-Shwartz, Sivan Sabato, S. Shalev-Shwartz, 2008, J. Mach. Learn. Res..

Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking

Shai Shalev-Shwartz, Sivan Sabato, S. Shalev-Shwartz, 2007, COLT.

SDCA without Duality pdf

Shai Shalev-Shwartz, S. Shalev-Shwartz, 2015, ArXiv.

Accelerated Mini-Batch Stochastic Dual Coordinate Ascent

Shai Shalev-Shwartz, Tong Zhang, S. Shalev-Shwartz, 2013, NIPS.

Distribution Free Learning with Local Queries

Amit Daniely, Shai Shalev-Shwartz, Galit Bary-Weisberg, 2016, ALT.

Agnostic Online Learning

Shai Ben-David, Shai Shalev-Shwartz, Dávid Pál, 2009, COLT.

Agnostic Online learnability

Shai Shalev-Shwartz, S. Shalev-Shwartz, 2008 .

Optimal learners for multiclass problems

Amit Daniely, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2014, COLT.

Strongly Adaptive Online Learning

Amit Daniely, Shai Shalev-Shwartz, Alon Gonen, 2015, ICML.

Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization

Shai Shalev-Shwartz, Alon Gonen, S. Shalev-Shwartz, 2016, J. Mach. Learn. Res..

On a Formal Model of Safe and Scalable Self-driving Cars pdf

Amnon Shashua, Shai Shalev-Shwartz, Shaked Shammah, 2017, ArXiv.

Robust temporal and spectral modeling for query By melody

Shlomo Dubnov, Yoram Singer, Shai Shalev-Shwartz, 2002, SIGIR '02.

Optimal Filtering of an Instrument Sound in a Mixed Recording Using Harmonic Model and Score Alignment

Michael Werman, Shlomo Dubnov, Shai Shalev-Shwartz, 2004 .

Failures of Gradient-Based Deep Learning

Ohad Shamir, Shai Shalev-Shwartz, Shaked Shammah, 2017, ICML.

The Implications of Local Correlation on Learning Some Deep Functions

Shai Shalev-Shwartz, Eran Malach, S. Shalev-Shwartz, 2020, NeurIPS.

Is Deeper Better only when Shallow is Good?

Shai Shalev-Shwartz, Eran Malach, S. Shalev-Shwartz, 2019, NeurIPS.

ShareBoost: Efficient multiclass learning with feature sharing

Amnon Shashua, Yonatan Wexler, Shai Shalev-Shwartz, 2011, NIPS.

SDCA without Duality, Regularization, and Individual Convexity pdf

Shai Shalev-Shwartz, S. Shalev-Shwartz, 2016, ICML.

SelfieBoost: A Boosting Algorithm for Deep Learning pdf

Shai Shalev-Shwartz, S. Shalev-Shwartz, 2014, ArXiv.

Computational Separation Between Convolutional and Fully-Connected Networks pdf

Shai Shalev-Shwartz, Eran Malach, S. Shalev-Shwartz, 2020, ICLR.

On the Ethics of Building AI in a Responsible Manner pdf

Shai Shalev-Shwartz, Amnon Shashua, Shaked Shammah, 2020, ArXiv.

Proving the Lottery Ticket Hypothesis: Pruning is All You Need

Gilad Yehudai, Ohad Shamir, Shai Shalev-Shwartz, 2020, ICML.

Minimizing the Maximal Loss: How and Why

Yonatan Wexler, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2016, ICML.

Stochastic Methods for l1-regularized Loss Minimization

S. Shalev-Shwartz, Ambuj Tewari, 2011, J. Mach. Learn. Res..

SubTuning: Efficient Finetuning for Multi-Task Learning

S. Shalev-Shwartz, Eran Malach, Gal Kaplun, 2023, ArXiv.

The Complexity of Learning Halfspaces using Generalized Linear Methods

Nathan Linial, Amit Daniely, Shai Shalev-Shwartz, 2012, COLT.

Online Learning of Noisy Data

Nicolò Cesa-Bianchi, Ohad Shamir, Shai Shalev-Shwartz, 2011, IEEE Transactions on Information Theory.

Faster SGD Using Sketched Conditioning pdf

Shai Shalev-Shwartz, Alon Gonen, S. Shalev-Shwartz, 2015, ArXiv.

Vanishing Component Analysis

Roi Livni, Shai Shalev-Shwartz, Amir Globerson, 2013, ICML.

Discriminative kernel-based phoneme sequence recognition

Samy Bengio, Yoram Singer, Shai Shalev-Shwartz, 2006, INTERSPEECH.

The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees

Yoram Singer, Shai Shalev-Shwartz, Ofer Dekel, 2004, NIPS.

Learning Kernel-Based Halfspaces with the Zero-One Loss

Ohad Shamir, Karthik Sridharan, Shai Shalev-Shwartz, 2010, COLT 2010.

Less is More: Selective Layer Finetuning with SubTuning

S. Shalev-Shwartz, Eran Malach, Gal Kaplun, 2023, 2302.06354.

SubTuning: Efficient Finetuning for Multi-Task Learning

S. Shalev-Shwartz, Eran Malach, Gal Kaplun, 2023, ArXiv.

Online multiclass learning by interclass hypothesis sharing

Yoram Singer, Shai Shalev-Shwartz, Shimon Ullman, 2006, ICML.

Stochastic Gradient Descent

Quoc V. Le, S. Shalev-Shwartz, S. Ben-David, 2014, Machine Learning with Neural Networks.

Learning Boolean Circuits with Neural Networks

S. Shalev-Shwartz, Eran Malach, 2019, ArXiv.

Knowledge Distillation: Bad Models Can Be Good Role Models

S. Shalev-Shwartz, Eran Malach, Preetum Nakkiran, 2022, NeurIPS.

Weight Sharing is Crucial to Succesful Optimization pdf

Ohad Shamir, Shai Shalev-Shwartz, Shaked Shammah, 2017, ArXiv.

Multiclass Learning Approaches: A Theoretical Comparison with Implications

Amit Daniely, Shai Shalev-Shwartz, Sivan Sabato, 2012, NIPS.

Subspace Learning with Partial Information

Yonina C. Eldar, Shai Shalev-Shwartz, Alon Gonen, 2014, J. Mach. Learn. Res..

A Provably Correct Algorithm for Deep Learning that Actually Works pdf

Shai Shalev-Shwartz, Eran Malach, S. Shalev-Shwartz, 2018, ArXiv.

K-means recovers ICA filters when independent components are sparse

Shai Shalev-Shwartz, Alon Vinnikov, S. Shalev-Shwartz, 2014, ICML.

Efficient Pool-Based Active Learning of Halfspaces pdf

Shai Shalev-Shwartz, Alon Gonen, Sivan Sabato, 2012, arXiv.org.

Efficient active learning of halfspaces: an aggressive approach

Shai Shalev-Shwartz, Alon Gonen, Sivan Sabato, 2012, J. Mach. Learn. Res..

Pegasos: Primal Estimated sub-GrAdient SOlver for SVM

Y. Singer, S. Shalev-Shwartz, Andrew Cotter, 2011, ICML.

The Kernelized Stochastic Batch Perceptron pdf

Nathan Srebro, Andrew Cotter, Shai Shalev-Shwartz, 2012, ICML.

Online Learning of Complex Prediction Problems Using Simultaneous Projections

Yoram Singer, Shai Shalev-Shwartz, Yonatan Amit, 2008, J. Mach. Learn. Res..

: Primal Estimated sub-GrAdient SOlver for SVM

S. Shalev-Shwartz, Y. Singer, N. Srebro, 2009 .

Learning Linear and Kernel Predictors with the 0-1 Loss Function

Ohad Shamir, Karthik Sridharan, Shai Shalev-Shwartz, 2011, IJCAI.

Matrix completion with the trace norm: learning, bounding, and transducing

Ohad Shamir, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2014, J. Mach. Learn. Res..

Using More Data to Speed-up Training Time pdf

Ohad Shamir, Shai Shalev-Shwartz, Eran Tromer, 2011, AISTATS.

Decoupling Gating from Linearity

S. Shalev-Shwartz, Eran Malach, Jonathan Fiat, 2018, ArXiv.

Multiclass Learnability and the ERM principle

S. Shalev-Shwartz, S. Ben-David, Sivan Sabato, 2011, COLT.

Phoneme alignment based on discriminative learning

Yoram Singer, Shai Shalev-Shwartz, Joseph Keshet, 2005, INTERSPEECH.

Faster Low-rank Approximation using Adaptive Gap-based Preconditioning pdf

Shai Shalev-Shwartz, Alon Gonen, S. Shalev-Shwartz, 2016, ArXiv.

Failures of Deep Learning pdf

Ohad Shamir, Shai Shalev-Shwartz, Shaked Shammah, 2017, ArXiv.

Active Learning of Halfspaces under a Margin Assumption

Shai Shalev-Shwartz, Alon Gonen, Sivan Sabato, 2011, 1112.1556.

Active Learning of Halfspaces

S. Shalev-Shwartz, 2013 .

Vision Zero: on a Provable Method for Eliminating Roadway Accidents without Compromising Traffic Throughput

Amnon Shashua, Shai Shalev-Shwartz, Shaked Shammah, 2018, 1901.05022.

Understanding Machine Learning - From Theory to Algorithms

S. Shalev-Shwartz, Shai Ben-David, 2014 .

An Algorithm for Training Polynomial Networks

Roi Livni, Ohad Shamir, Shai Shalev-Shwartz, 2013, 1304.7045.

On the Sample Complexity of End-to-end Training vs. Semantic Abstraction Training pdf

Amnon Shashua, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2016, ArXiv.

Learning a Metric Embedding for Face Recognition using the Multibatch Method

Amnon Shashua, Yonatan Wexler, Shai Shalev-Shwartz, 2016, NIPS.

24th Annual Conference on Learning Theory Multiclass Learnability

S. Shalev-Shwartz, Sivan Sabato, Shai Ben-David, 2022 .

Stochastic dual coordinate ascent methods for regularized loss

S. Shalev-Shwartz, Tong Zhang, 2012, J. Mach. Learn. Res..

Pegasos: primal estimated sub-gradient solver for SVM

Y. Singer, S. Shalev-Shwartz, Andrew Cotter, 2007, ICML '07.

Understanding Machine Learning - From Theory to Algorithms

S. Shalev-Shwartz, Shai Ben-David, Shai Shalev-Shwartz, 2014 .

The Complexity of Improperly Learning Large Margin Halfspaces

Ohad Shamir, Karthik Sridharan, Shai Shalev-Shwartz, 2009, COLT.

Learning Halfspaces with the Zero-One Loss: Time-Accuracy Tradeoffs

Shai Shalev-Shwartz, Aharon Birnbaum, S. Shalev-Shwartz, 2012, NIPS.

Learning the Experts for Online Sequence Prediction pdf

Elad Eban, Shai Shalev-Shwartz, Amir Globerson, 2012, ICML.

Online Learning of Noisy Data with Kernels

Ohad Shamir, Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, 2010, COLT 2010.

Managing AI Risks in an Era of Rapid Progress

S. Shalev-Shwartz, Tegan Maharaj, Y. Bengio, 2023, ArXiv.

A Provably Efficient Algorithm for Training Deep Networks pdf

Roi Livni, Ohad Shamir, Shai Shalev-Shwartz, 2013, ArXiv.

Efficient Learning of Label Ranking by Soft Projections onto Polyhedra

Yoram Singer, Shai Shalev-Shwartz, Y. Singer, 2006, J. Mach. Learn. Res..

Learnability and Stability in the General Learning Setting

Ohad Shamir, Nathan Srebro, Karthik Sridharan, 2009, COLT.