Shalev-Shwartz, Shai

92 publications

ICLR 2025 Jamba: Hybrid Transformer-Mamba Language Models Barak Lenz, Opher Lieber, Alan Arazi, Amir Bergman, Avshalom Manevich, Barak Peleg, Ben Aviram, Chen Almagor, Clara Fridman, Dan Padnos, Daniel Gissin, Daniel Jannai, Dor Muhlgay, Dor Zimberg, Edden M. Gerber, Elad Dolev, Eran Krakovsky, Erez Safahi, Erez Schwartz, Gal Cohen, Gal Shachaf, Haim Rozenblum, Hofit Bata, Ido Blass, Inbal Magar, Itay Dalmedigos, Jhonathan Osin, Julie Fadlon, Maria Rozman, Matan Danos, Michael Gokhman, Mor Zusman, Naama Gidron, Nir Ratner, Noam Gat, Noam Rozen, Oded Fried, Ohad Leshno, Omer Antverg, Omri Abend, Or Dagan, Orit Cohavi, Raz Alon, Ro'i Belson, Roi Cohen, Rom Gilad, Roman Glozman, Shahar Lev, Shai Shalev-Shwartz, Shaked Haim Meirom, Tal Delbari, Tal Ness, Tomer Asida, Tom Ben Gal, Tom Braude, Uriya Pumerantz, Josh Cohen, Yonatan Belinkov, Yuval Globerson, Yuval Peleg Levy, Yoav Shoham

ICMLW 2022 Huge Frozen Language Models as Readers for Open-Domain Question Answering Yoav Levine, Ori Ram, Daniel Jannai, Barak Lenz, Shai Shalev-Shwartz, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham

NeurIPS 2022 Knowledge Distillation: Bad Models Can Be Good Role Models Gal Kaplun, Eran Malach, Preetum Nakkiran, Shai Shalev-Shwartz

JMLR 2022 When Hardness of Approximation Meets Hardness of Learning Eran Malach, Shai Shalev-Shwartz

ICLR 2021 Computational Separation Between Convolutional and Fully-Connected Networks Eran Malach, Shai Shalev-Shwartz

ALT 2020 Distribution Free Learning with Local Queries Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz

NeurIPS 2020 The Implications of Local Correlation on Learning Some Deep Functions Eran Malach, Shai Shalev-Shwartz

ICLR 2020 The Implicit Bias of Depth: How Incremental Learning Drives Generalization Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely

NeurIPS 2019 Is Deeper Better Only When Shallow Is Good? Eran Malach, Shai Shalev-Shwartz

ICLR 2018 SGD Learns Over-Parameterized Networks That Provably Generalize on Linearly Separable Data Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz

NeurIPS 2017 Decoupling "when to Update" from "how to Update" Eran Malach, Shai Shalev-Shwartz

COLT 2017 Effective Semisupervised Learning on Manifolds Amir Globerson, Roi Livni, Shai Shalev-Shwartz

ICML 2017 Failures of Gradient-Based Deep Learning Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah

COLT 2017 Fast Rates for Empirical Risk Minimization of Strict Saddle Problems Alon Gonen, Shai Shalev-Shwartz

COLT 2016 Complexity Theoretic Limitations on Learning DNF's Amit Daniely, Shai Shalev-Shwartz

NeurIPS 2016 Learning a Metric Embedding for Face Recognition Using the Multibatch Method Oren Tadmor, Tal Rosenwein, Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua

ICML 2016 Minimizing the Maximal Loss: How and Why Shai Shalev-Shwartz, Yonatan Wexler

ICML 2016 On Graduated Optimization for Stochastic Non-Convex Problems Elad Hazan, Kfir Yehuda Levy, Shai Shalev-Shwartz

JMLR 2016 On Lower and Upper Bounds in Smooth and Strongly Convex Optimization Yossi Arjevani, Shai Shalev-Shwartz, Ohad Shamir

ICML 2016 SDCA Without Duality, Regularization, and Individual Convexity Shai Shalev-Shwartz

ICML 2016 Solving Ridge Regression Using Sketched Preconditioned SVRG Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz

JMLR 2016 Subspace Learning with Partial Information Alon Gonen, Dan Rosenbaum, Yonina C. Eldar, Shai Shalev-Shwartz

NeurIPS 2015 Beyond Convexity: Stochastic Quasi-Convex Optimization Elad Hazan, Kfir Levy, Shai Shalev-Shwartz

JMLR 2015 Learning Sparse Low-Threshold Linear Classifiers Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel Hsu, Tong Zhang

JMLR 2015 Multiclass Learnability and the ERM Principle Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz

ICML 2015 Strongly Adaptive Online Learning Amit Daniely, Alon Gonen, Shai Shalev-Shwartz

ICML 2014 Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization Shai Shalev-Shwartz, Tong Zhang

ICML 2014 K-Means Recovers ICA Filters When Independent Components Are Sparse Alon Vinnikov, Shai Shalev-Shwartz

JMLR 2014 Matrix Completion with the Trace Norm: Learning, Bounding, and Transducing Ohad Shamir, Shai Shalev-Shwartz

NeurIPS 2014 On the Computational Efficiency of Training Neural Networks Roi Livni, Shai Shalev-Shwartz, Ohad Shamir

COLT 2014 Optimal Learners for Multiclass Problems Amit Daniely, Shai Shalev-Shwartz

COLT 2014 The Complexity of Learning Halfspaces Using Generalized Linear Methods Amit Daniely, Nati Linial, Shai Shalev-Shwartz

NeurIPS 2013 Accelerated Mini-Batch Stochastic Dual Coordinate Ascent Shai Shalev-Shwartz, Tong Zhang

COLT 2013 COLT 2013 - The 26th Annual Conference on Learning Theory, June 12-14, 2013, Princeton University, NJ, USA Shai Shalev-Shwartz, Ingo Steinwart

ICML 2013 Efficient Active Learning of Halfspaces: An Aggressive Approach Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz

JMLR 2013 Efficient Active Learning of Halfspaces: An Aggressive Approach Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz

ICML 2013 Learning Optimally Sparse Support Vector Machines Andrew Cotter, Shai Shalev-Shwartz, Nati Srebro

NeurIPS 2013 More Data Speeds up Training Time in Learning Halfspaces over Sparse Vectors Amit Daniely, Nati Linial, Shai Shalev-Shwartz

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

ICML 2013 Vanishing Component Analysis Roi Livni, David Lehavi, Sagi Schein, Hila Nachliely, Shai Shalev-Shwartz, Amir Globerson

ALT 2012 Learnability Beyond Uniform Convergence Shai Shalev-Shwartz

ICML 2012 Learning the Experts for Online Sequence Prediction Elad Eban, Aharon Birnbaum, Shai Shalev-Shwartz, Amir Globerson

COLT 2012 Near-Optimal Algorithms for Online Matrix Prediction Elad Hazan, Satyen Kale, Shai Shalev-Shwartz

FnTML 2012 Online Learning and Online Convex Optimization Shai Shalev-Shwartz

JMLR 2012 Regularization Techniques for Learning with Matrices Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari

ICML 2012 The Kernelized Stochastic Batch Perceptron Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro

AISTATS 2012 Using More Data to Speed-up Training Time Shai Shalev-Shwartz, Ohad Shamir, Eran Tromer

ICML 2011 Access to Unlabeled Data Can Speed up Prediction Time Ruth Urner, Shai Shalev-Shwartz, Shai Ben-David

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

JMLR 2011 Efficient Learning with Partially Observed Attributes Nicoló Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir

ICML 2011 Large-Scale Convex Minimization with a Low-Rank Constraint Shai Shalev-Shwartz, Alon Gonen, Ohad Shamir

IJCAI 2011 Learning Linear and Kernel Predictors with the 0-1 Loss Function Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan

COLT 2011 Multiclass Learnability and the ERM Principle Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz

AAAI 2011 Quantity Makes Quality: Learning with Partial Views Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir

NeurIPS 2011 ShareBoost: Efficient Multiclass Learning with Feature Sharing Shai Shalev-shwartz, Yonatan Wexler, Amnon Shashua

JMLR 2011 Stochastic Methods for L1-Regularized Loss Minimization Shai Shalev-Shwartz, Ambuj Tewari

COLT 2010 Composite Objective Mirror Descent John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Ambuj Tewari

ICML 2010 Efficient Learning with Partially Observed Attributes Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir

JMLR 2010 Learnability, Stability and Uniform Convergence Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan

COLT 2010 Learning Kernel-Based Halfspaces with the Zero-One Loss Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan

MLJ 2010 On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms Shai Shalev-Shwartz, Yoram Singer

COLT 2010 Online Learning of Noisy Data with Kernels Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir

COLT 2009 Agnostic Online Learning Shai Ben-David, Dávid Pál, Shai Shalev-Shwartz

COLT 2009 Learnability and Stability in the General Learning Setting Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan

COLT 2009 Stochastic Convex Optimization Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan

ICML 2009 Stochastic Methods for L1 Regularized Loss Minimization Shai Shalev-Shwartz, Ambuj Tewari

COLT 2009 The Complexity of Improperly Learning Large Margin Halfspaces Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan

ICML 2008 Efficient Bandit Algorithms for Online Multiclass Prediction Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari

ICML 2008 Efficient Projections onto the L1-Ball for Learning in High Dimensions John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Tushar Chandra

NeurIPS 2008 Fast Rates for Regularized Objectives Karthik Sridharan, Shai Shalev-shwartz, Nathan Srebro

NeurIPS 2008 Mind the Duality Gap: Logarithmic Regret Algorithms for Online Optimization Shai Shalev-shwartz, Sham M. Kakade

COLT 2008 On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms Shai Shalev-Shwartz, Yoram Singer

JMLR 2008 Online Learning of Complex Prediction Problems Using Simultaneous Projections Yonatan Amit, Shai Shalev-Shwartz, Yoram Singer

JMLR 2008 Ranking Categorical Features Using Generalization Properties Sivan Sabato, Shai Shalev-Shwartz

ICML 2008 SVM Optimization: Inverse Dependence on Training Set Size Shai Shalev-Shwartz, Nathan Srebro

MLJ 2007 A Primal-Dual Perspective of Online Learning Algorithms Shai Shalev-Shwartz, Yoram Singer

AISTATS 2007 A Unified Algorithmic Approach for Efficient Online Label Ranking Shai Shalev-Shwartz, Yoram Singer

ICML 2007 Pegasos: Primal Estimated Sub-GrAdient SOlver for SVM Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro

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

NeurIPS 2006 Convex Repeated Games and Fenchel Duality Shai Shalev-shwartz, Yoram Singer

JMLR 2006 Efficient Learning of Label Ranking by Soft Projections onto Polyhedra Shai Shalev-Shwartz, Yoram Singer

NeurIPS 2006 Online Classification for Complex Problems Using Simultaneous Projections Yonatan Amit, Shai Shalev-shwartz, Yoram Singer

COLT 2006 Online Learning Meets Optimization in the Dual Shai Shalev-Shwartz, Yoram Singer

ICML 2006 Online Multiclass Learning by Interclass Hypothesis Sharing Michael Fink, Shai Shalev-Shwartz, Yoram Singer, Shimon Ullman

JMLR 2006 Online Passive-Aggressive Algorithms Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer

COLT 2005 A New Perspective on an Old Perceptron Algorithm Shai Shalev-Shwartz, Yoram Singer

JMLR 2005 Smooth Ε-Insensitive Regression by Loss Symmetrization Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer

NeurIPS 2005 The Forgetron: A Kernel-Based Perceptron on a Fixed Budget Ofer Dekel, Shai Shalev-shwartz, Yoram Singer

ICML 2004 Online and Batch Learning of Pseudo-Metrics Shai Shalev-Shwartz, Yoram Singer, Andrew Y. Ng

NeurIPS 2004 The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees Ofer Dekel, Shai Shalev-shwartz, Yoram Singer

NeurIPS 2003 Online Passive-Aggressive Algorithms Shai Shalev-shwartz, Koby Crammer, Ofer Dekel, Yoram Singer

COLT 2003 Smooth E-Intensive Regression by Loss Symmetrization Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer