Shai Shalev-Shwartz

190 papers A* 54A 4B 2Misc 1Journal 104Unranked 21

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	FormulaOne: Measuring the Depth of Algorithmic Reasoning Beyond Competitive Programming. CoRR Gal Beniamini, Yuval Dor, Alon Vinnikov, Shir Granot Peled, Or Weinstein, Or Sharir, Noam Wies, Tomer Nussbaum, Ido Ben-Shaul, Tomer Zekharya, Yoav Levine, Shai Shalev-Shwartz, Amnon Shashua
2025	J	`jnl`	From Reasoning to Super-Intelligence: A Search-Theoretic Perspective. CoRR Shai Shalev-Shwartz, Amnon Shashua
2025	J	`jnl`	Research Program: Theory of Learning in Dynamical Systems. CoRR Elad Hazan, Shai Shalev-Shwartz, Nathan Srebro
2024	J	`jnl`	Artificial Expert Intelligence through PAC-reasoning. CoRR Shai Shalev-Shwartz, Amnon Shashua, Gal Beniamini, Yoav Levine, Or Sharir, Noam Wies, Ido Ben-Shaul, Tomer Nussbaum, Shir Granot Peled
2024	J	`jnl`	Jamba: A Hybrid Transformer-Mamba Language Model. CoRR Opher Lieber, Barak Lenz, Hofit Bata, Gal Cohen, Jhonathan Osin, Itay Dalmedigos, Erez Safahi, Shaked Meirom, Yonatan Belinkov, Shai Shalev-Shwartz, Omri Abend, Raz Alon, Tomer Asida, Amir Bergman, Roman Glozman, Michael Gokhman, Avashalom Manevich, Nir Ratner, Noam Rozen, Erez Shwartz, Mor Zusman, Yoav Shoham
2024	J	`jnl`	Untangling Lariats: Subgradient Following of Variationally Penalized Objectives. CoRR Kai-Chia Mo, Shai Shalev-Shwartz, Nisæl Shártov
2023	J	`jnl`	Managing AI Risks in an Era of Rapid Progress. CoRR Yoshua Bengio, Geoffrey E. Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, Gillian K. Hadfield, Jeff Clune, Tegan Maharaj, Frank Hutter, Atilim Günes Baydin, Sheila A. McIlraith, Qiqi Gao, Ashwin Acharya, David Krueger, Anca D. Dragan, Philip H. S. Torr, Stuart Russell, Daniel Kahneman, Jan Brauner, Sören Mindermann
2023	J	`jnl`	SubTuning: Efficient Finetuning for Multi-Task Learning. CoRR Gal Kaplun, Andrey Gurevich, Tal Swisa, Mazor David, Shai Shalev-Shwartz, Eran Malach
2022	A*	`conf`	Efficient Learning of CNNs using Patch Based Features. ICML Alon Brutzkus, Amir Globerson, Eran Malach, Alon Regev Netser, Shai Shalev-Shwartz
2022	A*	`conf`	Knowledge Distillation: Bad Models Can Be Good Role Models. NeurIPS Gal Kaplun, Eran Malach, Preetum Nakkiran, Shai Shalev-Shwartz
2022	J	`jnl`	Knowledge Distillation: Bad Models Can Be Good Role Models. CoRR Gal Kaplun, Eran Malach, Preetum Nakkiran, Shai Shalev-Shwartz
2022	J	`jnl`	MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning. CoRR Ehud Karpas, Omri Abend, Yonatan Belinkov, Barak Lenz, Opher Lieber, Nir Ratner, Yoav Shoham, Hofit Bata, Yoav Levine, Kevin Leyton-Brown, Dor Muhlgay, Noam Rozen, Erez Schwartz, Gal Shachaf, Shai Shalev-Shwartz, Amnon Shashua, Moshe Tennenholtz
2022	J	`jnl`	Standing on the Shoulders of Giant Frozen Language Models. CoRR Yoav Levine, Itay Dalmedigos, Ori Ram, Yoel Zeldes, Daniel Jannai, Dor Muhlgay, Yoni Osin, Opher Lieber, Barak Lenz, Shai Shalev-Shwartz, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham
2022	J	`jnl`	When Hardness of Approximation Meets Hardness of Learning. J. Mach. Learn. Res. Eran Malach, Shai Shalev-Shwartz
2021	A*	`conf`	Computational Separation Between Convolutional and Fully-Connected Networks. ICLR Eran Malach, Shai Shalev-Shwartz
2021	A*	`conf`	The Connection Between Approximation, Depth Separation and Learnability in Neural Networks. COLT Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir
2021	J	`jnl`	The Connection Between Approximation, Depth Separation and Learnability in Neural Networks. CoRR Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir
2020	J	`jnl`	Computational Separation Between Convolutional and Fully-Connected Networks. CoRR Eran Malach, Shai Shalev-Shwartz
2020	B	`conf`	Distribution Free Learning with Local Queries. ALT Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz
2020	J	`jnl`	On the Ethics of Building AI in a Responsible Manner. CoRR Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua
2020	A*	`conf`	Proving the Lottery Ticket Hypothesis: Pruning is All You Need. ICML Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir
2020	J	`jnl`	Proving the Lottery Ticket Hypothesis: Pruning is All You Need. CoRR Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir
2020	A*	`conf`	SenseBERT: Driving Some Sense into BERT. ACL Yoav Levine, Barak Lenz, Or Dagan, Ori Ram, Dan Padnos, Or Sharir, Shai Shalev-Shwartz, Amnon Shashua, Yoav Shoham
2020	A*	`conf`	The Implications of Local Correlation on Learning Some Deep Functions. NeurIPS Eran Malach, Shai Shalev-Shwartz
2020	A*	`conf`	The Implicit Bias of Depth: How Incremental Learning Drives Generalization. ICLR Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely
2020	J	`jnl`	When Hardness of Approximation Meets Hardness of Learning. CoRR Eran Malach, Shai Shalev-Shwartz
2019	J	`jnl`	Decoupling Gating from Linearity. CoRR Jonathan Fiat, Eran Malach, Shai Shalev-Shwartz
2019	J	`jnl`	Discriminative Active Learning. CoRR Daniel Gissin, Shai Shalev-Shwartz
2019	A*	`conf`	Is Deeper Better only when Shallow is Good? NeurIPS Eran Malach, Shai Shalev-Shwartz
2019	J	`jnl`	Is Deeper Better only when Shallow is Good? CoRR Eran Malach, Shai Shalev-Shwartz
2019	J	`jnl`	Learning Boolean Circuits with Neural Networks. CoRR Eran Malach, Shai Shalev-Shwartz
2019	J	`jnl`	SenseBERT: Driving Some Sense into BERT. CoRR Yoav Levine, Barak Lenz, Or Dagan, Dan Padnos, Or Sharir, Shai Shalev-Shwartz, Amnon Shashua, Yoav Shoham
2019	J	`jnl`	The Implicit Bias of Depth: How Incremental Learning Drives Generalization. CoRR Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely
2019	J	`jnl`	Vision Zero: on a Provable Method for Eliminating Roadway Accidents without Compromising Traffic Throughput. CoRR Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua
2018	J	`jnl`	A Provably Correct Algorithm for Deep Learning that Actually Works. CoRR Eran Malach, Shai Shalev-Shwartz
2018	—	`conf`	SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data. ICLR (Poster) Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz
2017	J	`jnl`	Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization. J. Mach. Learn. Res. Alon Gonen, Shai Shalev-Shwartz
2017	—	`conf`	Decoupling "when to update" from "how to update". NIPS Eran Malach, Shai Shalev-Shwartz
2017	J	`jnl`	Decoupling "when to update" from "how to update". CoRR Eran Malach, Shai Shalev-Shwartz
2017	A*	`conf`	Effective Semisupervised Learning on Manifolds. COLT Amir Globerson, Roi Livni, Shai Shalev-Shwartz
2017	J	`jnl`	Failures of Deep Learning. CoRR Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah
2017	A*	`conf`	Failures of Gradient-Based Deep Learning. ICML Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah
2017	A*	`conf`	Fast Rates for Empirical Risk Minimization of Strict Saddle Problems. COLT Alon Gonen, Shai Shalev-Shwartz
2017	J	`jnl`	Fast Rates for Empirical Risk Minimization of Strict Saddle Problems. CoRR Alon Gonen, Shai Shalev-Shwartz
2017	J	`jnl`	Near-Optimal Algorithms for Online Matrix Prediction. SIAM J. Comput. Elad Hazan, Satyen Kale, Shai Shalev-Shwartz
2017	J	`jnl`	On a Formal Model of Safe and Scalable Self-driving Cars. CoRR Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua
2017	J	`jnl`	SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data. CoRR Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz
2017	J	`jnl`	Weight Sharing is Crucial to Succesful Optimization. CoRR Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah
2016	J	`jnl`	Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization. Math. Program. Shai Shalev-Shwartz, Tong Zhang
2016	A*	`conf`	Complexity Theoretic Limitations on Learning DNF's. COLT Amit Daniely, Shai Shalev-Shwartz
2016	J	`jnl`	Distribution Free Learning with Local Queries. CoRR Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz
2016	J	`jnl`	Faster Low-rank Approximation using Adaptive Gap-based Preconditioning. CoRR Alon Gonen, Shai Shalev-Shwartz
2016	—	`conf`	Learning a Metric Embedding for Face Recognition using the Multibatch Method. NIPS Oren Tadmor, Tal Rosenwein, Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua
2016	J	`jnl`	Learning a Metric Embedding for Face Recognition using the Multibatch Method. CoRR Oren Tadmor, Yonatan Wexler, Tal Rosenwein, Shai Shalev-Shwartz, Amnon Shashua
2016	J	`jnl`	Long-term Planning by Short-term Prediction. CoRR Shai Shalev-Shwartz, Nir Ben-Zrihem, Aviad Cohen, Amnon Shashua
2016	A*	`conf`	Minimizing the Maximal Loss: How and Why. ICML Shai Shalev-Shwartz, Yonatan Wexler
2016	J	`jnl`	Minimizing the Maximal Loss: How and Why? CoRR Shai Shalev-Shwartz, Yonatan Wexler
2016	A*	`conf`	On Graduated Optimization for Stochastic Non-Convex Problems. ICML Elad Hazan, Kfir Yehuda Levy, Shai Shalev-Shwartz
2016	J	`jnl`	On Lower and Upper Bounds in Smooth and Strongly Convex Optimization. J. Mach. Learn. Res. Yossi Arjevani, Shai Shalev-Shwartz, Ohad Shamir
2016	J	`jnl`	On the Sample Complexity of End-to-end Training vs. Semantic Abstraction Training. CoRR Shai Shalev-Shwartz, Amnon Shashua
2016	—	`ch.`	Perceptron Algorithm. Encyclopedia of Algorithms Shai Shalev-Shwartz
2016	A*	`conf`	SDCA without Duality, Regularization, and Individual Convexity. ICML Shai Shalev-Shwartz
2016	J	`jnl`	SDCA without Duality, Regularization, and Individual Convexity. CoRR Shai Shalev-Shwartz
2016	J	`jnl`	Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving. CoRR Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua
2016	A*	`conf`	Solving Ridge Regression using Sketched Preconditioned SVRG. ICML Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz
2016	J	`jnl`	Solving Ridge Regression using Sketched Preconditioned SVRG. CoRR Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz
2016	J	`jnl`	Subspace Learning with Partial Information. J. Mach. Learn. Res. Alon Gonen, Dan Rosenbaum, Yonina C. Eldar, Shai Shalev-Shwartz
2016	J	`jnl`	Tightening the Sample Complexity of Empirical Risk Minimization via Preconditioned Stability. CoRR Alon Gonen, Shai Shalev-Shwartz
2015	—	`conf`	Beyond Convexity: Stochastic Quasi-Convex Optimization. NIPS Elad Hazan, Kfir Y. Levy, Shai Shalev-Shwartz
2015	J	`jnl`	Beyond Convexity: Stochastic Quasi-Convex Optimization. CoRR Elad Hazan, Kfir Y. Levy, Shai Shalev-Shwartz
2015	J	`jnl`	Faster SGD Using Sketched Conditioning. CoRR Alon Gonen, Shai Shalev-Shwartz
2015	J	`jnl`	Learning sparse low-threshold linear classifiers. J. Mach. Learn. Res. Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel J. Hsu, Tong Zhang
2015	J	`jnl`	Multiclass learnability and the ERM principle. J. Mach. Learn. Res. Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz
2015	J	`jnl`	On Graduated Optimization for Stochastic Non-Convex Problems. CoRR Elad Hazan, Kfir Y. Levy, Shai Shalev-Shwartz
2015	J	`jnl`	On Lower and Upper Bounds for Smooth and Strongly Convex Optimization Problems. CoRR Yossi Arjevani, Shai Shalev-Shwartz, Ohad Shamir
2015	J	`jnl`	SDCA without Duality. CoRR Shai Shalev-Shwartz
2015	A*	`conf`	Strongly Adaptive Online Learning. ICML Amit Daniely, Alon Gonen, Shai Shalev-Shwartz
2015	J	`jnl`	Strongly Adaptive Online Learning. CoRR Amit Daniely, Alon Gonen, Shai Shalev-Shwartz
2014	A*	`conf`	Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization. ICML Shai Shalev-Shwartz, Tong Zhang
2014	J	`jnl`	Complexity theoretic limitations on learning DNF's. CoRR Amit Daniely, Shai Shalev-Shwartz
2014	A*	`conf`	From average case complexity to improper learning complexity. STOC Amit Daniely, Nati Linial, Shai Shalev-Shwartz
2014	A*	`conf`	K-means recovers ICA filters when independent components are sparse. ICML Alon Vinnikov, Shai Shalev-Shwartz
2014	J	`jnl`	Matrix completion with the trace norm: learning, bounding, and transducing. J. Mach. Learn. Res. Ohad Shamir, Shai Shalev-Shwartz
2014	—	`conf`	On the Computational Efficiency of Training Neural Networks. NIPS Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
2014	J	`jnl`	On the Computational Efficiency of Training Neural Networks. CoRR Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
2014	J	`jnl`	Optimal Learners for Multiclass Problems. CoRR Amit Daniely, Shai Shalev-Shwartz
2014	A*	`conf`	Optimal learners for multiclass problems. COLT Amit Daniely, Shai Shalev-Shwartz
2014	J	`jnl`	SelfieBoost: A Boosting Algorithm for Deep Learning. CoRR Shai Shalev-Shwartz
2014	A*	`conf`	The Complexity of Learning Halfspaces using Generalized Linear Methods. COLT Amit Daniely, Nati Linial, Shai Shalev-Shwartz
2014	J	`jnl`	The Sample Complexity of Subspace Learning with Partial Information. CoRR Alon Gonen, Dan Rosenbaum, Yonina C. Eldar, Shai Shalev-Shwartz
2014	—	`book`	Understanding Machine Learning - From Theory to Algorithms. Shai Shalev-Shwartz, Shai Ben-David
2013	J	`jnl`	A Provably Efficient Algorithm for Training Deep Networks CoRR Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
2013	J	`jnl`	Accelerated Mini-Batch Stochastic Dual Coordinate Ascent CoRR Shai Shalev-Shwartz, Tong Zhang
2013	—	`conf`	Accelerated Mini-Batch Stochastic Dual Coordinate Ascent. NIPS Shai Shalev-Shwartz, Tong Zhang
2013	J	`jnl`	Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization. CoRR Shai Shalev-Shwartz, Tong Zhang
2013	A*	`ed.`	COLT 2013 - The 26th Annual Conference on Learning Theory, June 12-14, 2013, Princeton University, NJ, USA COLT Shai Shalev-Shwartz, Ingo Steinwart
2013	—	`conf`	Efficient Active Learning of Halfspaces: an Aggressive Approach. ICML (1) Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz
2013	J	`jnl`	Efficient active learning of halfspaces: an aggressive approach. J. Mach. Learn. Res. Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz
2013	J	`jnl`	From average case complexity to improper learning complexity. CoRR Amit Daniely, Nati Linial, Shai Shalev-Shwartz
2013	—	`conf`	Learning Optimally Sparse Support Vector Machines. ICML (1) Andrew Cotter, Shai Shalev-Shwartz, Nati Srebro
2013	—	`conf`	More data speeds up training time in learning halfspaces over sparse vectors. NIPS Amit Daniely, Nati Linial, Shai Shalev-Shwartz
2013	J	`jnl`	More data speeds up training time in learning halfspaces over sparse vectors. CoRR Amit Daniely, Nati Linial, Shai Shalev-Shwartz
2013	J	`jnl`	Multiclass learnability and the ERM principle. CoRR Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz
2013	J	`jnl`	Stochastic dual coordinate ascent methods for regularized loss. J. Mach. Learn. Res. Shai Shalev-Shwartz, Tong Zhang
2013	—	`conf`	Vanishing Component Analysis. ICML (1) Roi Livni, David Lehavi, Sagi Schein, Hila Nachlieli, Shai Shalev-Shwartz, Amir Globerson
2012	Misc	`conf`	Domain Adaptation--Can Quantity compensate for Quality?. ISAIM Shai Ben-David, Shai Shalev-Shwartz, Ruth Urner
2012	J	`jnl`	Efficient Pool-Based Active Learning of Halfspaces CoRR Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz
2012	B	`conf`	Learnability beyond Uniform Convergence. ALT Shai Shalev-Shwartz
2012	—	`conf`	Learning Halfspaces with the Zero-One Loss: Time-Accuracy Tradeoffs. NIPS Aharon Birnbaum, Shai Shalev-Shwartz
2012	J	`jnl`	Learning Sparse Low-Threshold Linear Classifiers CoRR Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel J. Hsu, Tong Zhang
2012	J	`jnl`	Learning the Experts for Online Sequence Prediction CoRR Elad Eban, Aharon Birnbaum, Shai Shalev-Shwartz, Amir Globerson
2012	A*	`conf`	Learning the Experts for Online Sequence Prediction. ICML Elad Eban, Aharon Birnbaum, Shai Shalev-Shwartz, Amir Globerson
2012	J	`jnl`	Multiclass Learning Approaches: A Theoretical Comparison with Implications CoRR Amit Daniely, Sivan Sabato, Shai Shalev-Shwartz
2012	—	`conf`	Multiclass Learning Approaches: A Theoretical Comparison with Implications. NIPS Amit Daniely, Sivan Sabato, Shai Shalev-Shwartz
2012	J	`jnl`	Near-Optimal Algorithms for Online Matrix Prediction CoRR Elad Hazan, Satyen Kale, Shai Shalev-Shwartz
2012	A*	`conf`	Near-Optimal Algorithms for Online Matrix Prediction. COLT Elad Hazan, Satyen Kale, Shai Shalev-Shwartz
2012	J	`jnl`	Online Learning and Online Convex Optimization. Found. Trends Mach. Learn. Shai Shalev-Shwartz
2012	J	`jnl`	Proximal Stochastic Dual Coordinate Ascent CoRR Shai Shalev-Shwartz, Tong Zhang
2012	J	`jnl`	Regularization Techniques for Learning with Matrices. J. Mach. Learn. Res. Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari
2012	J	`jnl`	Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization CoRR Shai Shalev-Shwartz, Tong Zhang
2012	J	`jnl`	The Kernelized Stochastic Batch Perceptron CoRR Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro
2012	A*	`conf`	The Kernelized Stochastic Batch Perceptron. ICML Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro
2012	J	`jnl`	The error rate of learning halfspaces using Kernel-SVMs CoRR Amit Daniely, Nati Linial, Shai Shalev-Shwartz
2012	A	`conf`	Using More Data to Speed-up Training Time. AISTATS Shai Shalev-Shwartz, Ohad Shamir, Eran Tromer
2011	A*	`conf`	Access to Unlabeled Data can Speed up Prediction Time. ICML Ruth Urner, Shai Shalev-Shwartz, Shai Ben-David
2011	J	`jnl`	Active Learning Halfspaces under Margin Assumptions CoRR Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz
2011	A*	`conf`	Collaborative Filtering with the Trace Norm: Learning, Bounding, and Transducing. COLT Ohad Shamir, Shai Shalev-Shwartz
2011	J	`jnl`	Efficient Learning with Partially Observed Attributes. J. Mach. Learn. Res. Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir
2011	J	`jnl`	Large-Scale Convex Minimization with a Low-Rank Constraint CoRR Shai Shalev-Shwartz, Alon Gonen, Ohad Shamir
2011	A*	`conf`	Large-Scale Convex Minimization with a Low-Rank Constraint. ICML Shai Shalev-Shwartz, Alon Gonen, Ohad Shamir
2011	J	`jnl`	Learning Kernel-Based Halfspaces with the 0-1 Loss. SIAM J. Comput. Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan
2011	A*	`conf`	Learning Linear and Kernel Predictors with the 0-1 Loss Function. IJCAI Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan
2011	A*	`conf`	Multiclass Learnability and the ERM principle. COLT Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz
2011	J	`jnl`	Online Learning of Noisy Data. IEEE Trans. Inf. Theory Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir
2011	J	`jnl`	Pegasos: primal estimated sub-gradient solver for SVM. Math. Program. Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro, Andrew Cotter
2011	A*	`conf`	Quantity Makes Quality: Learning with Partial Views. AAAI Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir
2011	J	`jnl`	ShareBoost: Efficient Multiclass Learning with Feature Sharing CoRR Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua
2011	—	`conf`	ShareBoost: Efficient multiclass learning with feature sharing. NIPS Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua
2011	J	`jnl`	Stochastic Methods for J. Mach. Learn. Res. Shai Shalev-Shwartz, Ambuj Tewari
2011	J	`jnl`	Using More Data to Speed-up Training Time CoRR Shai Shalev-Shwartz, Ohad Shamir, Eran Tromer
2010	A*	`conf`	Composite Objective Mirror Descent. COLT John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Ambuj Tewari
2010	J	`jnl`	Efficient Learning with Partially Observed Attributes CoRR Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir
2010	A*	`conf`	Efficient Learning with Partially Observed Attributes. ICML Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir
2010	J	`jnl`	Learnability, Stability and Uniform Convergence. J. Mach. Learn. Res. Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan
2010	J	`jnl`	Learning Kernel-Based Halfspaces with the Zero-One Loss CoRR Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan
2010	A*	`conf`	Learning Kernel-Based Halfspaces with the Zero-One Loss. COLT Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan
2010	J	`jnl`	On the equivalence of weak learnability and linear separability: new relaxations and efficient boosting algorithms. Mach. Learn. Shai Shalev-Shwartz, Yoram Singer
2010	J	`jnl`	Online Learning of Noisy Data with Kernels CoRR Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir
2010	A*	`conf`	Online Learning of Noisy Data with Kernels. COLT Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir
2010	J	`jnl`	Trading Accuracy for Sparsity in Optimization Problems with Sparsity Constraints. SIAM J. Optim. Shai Shalev-Shwartz, Nathan Srebro, Tong Zhang
2009	A*	`conf`	Agnostic Online Learning. COLT Shai Ben-David, Dávid Pál, Shai Shalev-Shwartz
2009	J	`jnl`	Applications of strong convexity--strong smoothness duality to learning with matrices CoRR Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari
2009	J	`jnl`	Individual sequence prediction using memory-efficient context trees. IEEE Trans. Inf. Theory Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer
2009	A*	`conf`	Learnability and Stability in the General Learning Setting. COLT Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan
2009	A*	`conf`	Stochastic Convex Optimization. COLT Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan
2009	A*	`conf`	Stochastic methods for ICML Shai Shalev-Shwartz, Ambuj Tewari
2009	A*	`conf`	The Complexity of Improperly Learning Large Margin Halfspaces. COLT Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan
2008	A*	`conf`	Efficient bandit algorithms for online multiclass prediction. ICML Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari
2008	A*	`conf`	Efficient projections onto the ICML John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Tushar Chandra
2008	—	`conf`	Fast Rates for Regularized Objectives. NIPS Karthik Sridharan, Shai Shalev-Shwartz, Nathan Srebro
2008	—	`conf`	Mind the Duality Gap: Logarithmic regret algorithms for online optimization. NIPS Shai Shalev-Shwartz, Sham M. Kakade
2008	A*	`conf`	On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms. COLT Shai Shalev-Shwartz, Yoram Singer
2008	J	`jnl`	Online Learning of Complex Prediction Problems Using Simultaneous Projections. J. Mach. Learn. Res. Yonatan Amit, Shai Shalev-Shwartz, Yoram Singer
2008	—	`ch.`	Perceptron Algorithm. Encyclopedia of Algorithms Shai Shalev-Shwartz
2008	J	`jnl`	Ranking Categorical Features Using Generalization Properties. J. Mach. Learn. Res. Sivan Sabato, Shai Shalev-Shwartz
2008	A*	`conf`	SVM optimization: inverse dependence on training set size. ICML Shai Shalev-Shwartz, Nathan Srebro
2008	J	`jnl`	The Forgetron: A Kernel-Based Perceptron on a Budget. SIAM J. Comput. Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer
2007	J	`jnl`	A Large Margin Algorithm for Speech-to-Phoneme and Music-to-Score Alignment. IEEE Trans. Speech Audio Process. Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer, Dan Chazan
2007	A	`conf`	A Unified Algorithmic Approach for Efficient Online Label Ranking. AISTATS Shai Shalev-Shwartz, Yoram Singer
2007	J	`jnl`	A primal-dual perspective of online learning algorithms. Mach. Learn. Shai Shalev-Shwartz, Yoram Singer
2007	—	`—`	Online learning: theory, algorithms and applications (למידה מקוונת.). Shai Shalev-Shwartz
2007	A*	`conf`	Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. ICML Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro
2007	A*	`conf`	Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking. COLT Sivan Sabato, Shai Shalev-Shwartz
2006	—	`conf`	Convex Repeated Games and Fenchel Duality. NIPS Shai Shalev-Shwartz, Yoram Singer
2006	A	`conf`	Discriminative kernel-based phoneme sequence recognition. INTERSPEECH Joseph Keshet, Shai Shalev-Shwartz, Samy Bengio, Yoram Singer, Dan Chazan
2006	J	`jnl`	Efficient Learning of Label Ranking by Soft Projections onto Polyhedra. J. Mach. Learn. Res. Shai Shalev-Shwartz, Yoram Singer
2006	—	`conf`	Online Classification for Complex Problems Using Simultaneous Projections. NIPS Yonatan Amit, Shai Shalev-Shwartz, Yoram Singer
2006	A*	`conf`	Online Learning Meets Optimization in the Dual. COLT Shai Shalev-Shwartz, Yoram Singer
2006	J	`jnl`	Online Passive-Aggressive Algorithms. J. Mach. Learn. Res. Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer
2006	A*	`conf`	Online multiclass learning by interclass hypothesis sharing. ICML Michael Fink, Shai Shalev-Shwartz, Yoram Singer, Shimon Ullman
2005	A*	`conf`	A New Perspective on an Old Perceptron Algorithm. COLT Shai Shalev-Shwartz, Yoram Singer
2005	A	`conf`	Phoneme alignment based on discriminative learning. INTERSPEECH Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer, Dan Chazan
2005	J	`jnl`	Smooth epsiloon-Insensitive Regression by Loss Symmetrization. J. Mach. Learn. Res. Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer
2005	—	`conf`	The Forgetron: A Kernel-Based Perceptron on a Fixed Budget. NIPS Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer
2004	—	`conf`	Learning to Align Polyphonic Music. ISMIR Shai Shalev-Shwartz, Joseph Keshet, Yoram Singer
2004	A*	`conf`	Online and batch learning of pseudo-metrics. ICML Shai Shalev-Shwartz, Yoram Singer, Andrew Y. Ng
2004	—	`conf`	The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees. NIPS Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer
2003	—	`conf`	Online Passive-Aggressive Algorithms. NIPS Shai Shalev-Shwartz, Koby Crammer, Ofer Dekel, Yoram Singer
2003	A*	`conf`	Smooth e-Intensive Regression by Loss Symmetrization. COLT Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer
2002	A*	`conf`	Robust temporal and spectral modeling for query By melody. SIGIR Shai Shalev-Shwartz, Shlomo Dubnov, Nir Friedman, Yoram Singer