| 2022 | ICML | Efficient Learning of CNNs using Patch Based Features. | Alon Brutzkus, Amir Globerson, Eran Malach, Alon Regev Netser, Shai Shalev-Shwartz |
| 2021 | COLT | The Connection Between Approximation, Depth Separation and Learnability in Neural Networks. | Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir |
| 2021 | ICLR | Computational Separation Between Convolutional and Fully-Connected Networks. | Eran Malach, Shai Shalev-Shwartz |
| 2020 | ACL | SenseBERT: Driving Some Sense into BERT. | Yoav Levine, Barak Lenz, Or Dagan, Ori Ram, Dan Padnos, Or Sharir, Shai Shalev-Shwartz, Amnon Shashua, Yoav Shoham |
| 2020 | ALT | Distribution Free Learning with Local Queries. | Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz |
| 2020 | ICLR | The Implicit Bias of Depth: How Incremental Learning Drives Generalization. | Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely |
| 2020 | ICML | Proving the Lottery Ticket Hypothesis: Pruning is All You Need. | Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir |
| 2018 | ICLR | SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data. | Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz |
| 2017 | COLT | Effective Semisupervised Learning on Manifolds. | Amir Globerson, Roi Livni, Shai Shalev-Shwartz |
| 2017 | COLT | Fast Rates for Empirical Risk Minimization of Strict Saddle Problems. | Alon Gonen, Shai Shalev-Shwartz |
| 2017 | ICML | Failures of Gradient-Based Deep Learning. | Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah |
| 2016 | COLT | Complexity Theoretic Limitations on Learning DNF's. | Amit Daniely, Shai Shalev-Shwartz |
| 2016 | ICML | Solving Ridge Regression using Sketched Preconditioned SVRG. | Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz |
| 2016 | ICML | On Graduated Optimization for Stochastic Non-Convex Problems. | Elad Hazan, Kfir Yehuda Levy, Shai Shalev-Shwartz |
| 2016 | ICML | SDCA without Duality, Regularization, and Individual Convexity. | Shai Shalev-Shwartz |
| 2016 | ICML | Minimizing the Maximal Loss: How and Why. | Shai Shalev-Shwartz, Yonatan Wexler |
| 2015 | ICML | Strongly Adaptive Online Learning. | Amit Daniely, Alon Gonen, Shai Shalev-Shwartz |
| 2014 | COLT | The Complexity of Learning Halfspaces using Generalized Linear Methods. | Amit Daniely, Nati Linial, Shai Shalev-Shwartz |
| 2014 | COLT | Optimal learners for multiclass problems. | Amit Daniely, Shai Shalev-Shwartz |
| 2014 | ICML | Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization. | Shai Shalev-Shwartz, Tong Zhang |
| 2014 | ICML | K-means recovers ICA filters when independent components are sparse. | Alon Vinnikov, Shai Shalev-Shwartz |
| 2014 | STOC | From average case complexity to improper learning complexity. | Amit Daniely, Nati Linial, Shai Shalev-Shwartz |
| 2013 | ICML | Learning Optimally Sparse Support Vector Machines. | Andrew Cotter, Shai Shalev-Shwartz, Nati Srebro |
| 2013 | ICML | Efficient Active Learning of Halfspaces: an Aggressive Approach. | Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz |
| 2013 | ICML | Vanishing Component Analysis. | Roi Livni, David Lehavi, Sagi Schein, Hila Nachlieli, Shai Shalev-Shwartz, Amir Globerson |
| 2012 | ALT | Learnability beyond Uniform Convergence. | Shai Shalev-Shwartz |
| 2012 | ICML | The Kernelized Stochastic Batch Perceptron. | Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro |
| 2012 | ICML | Learning the Experts for Online Sequence Prediction. | Elad Eban, Aharon Birnbaum, Shai Shalev-Shwartz, Amir Globerson |
| 2012 | ISAIM | Domain Adaptation--Can Quantity compensate for Quality?. | Shai Ben-David, Shai Shalev-Shwartz, Ruth Urner |
| 2011 | AAAI | Quantity Makes Quality: Learning with Partial Views. | Nicol Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir |
| 2011 | ICML | Large-Scale Convex Minimization with a Low-Rank Constraint. | Shai Shalev-Shwartz, Alon Gonen, Ohad Shamir |
| 2011 | ICML | Access to Unlabeled Data can Speed up Prediction Time. | Ruth Urner, Shai Shalev-Shwartz, Shai Ben-David |
| 2011 | IJCAI | Learning Linear and Kernel Predictors with the 0-1 Loss Function. | Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan |
| 2010 | COLT | Online Learning of Noisy Data with Kernels. | Nicol Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir |
| 2010 | COLT | Composite Objective Mirror Descent. | John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Ambuj Tewari |
| 2010 | COLT | Learning Kernel-Based Halfspaces with the Zero-One Loss. | Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan |
| 2010 | ICML | Efficient Learning with Partially Observed Attributes. | Nicol Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir |
| 2009 | COLT | Agnostic Online Learning. | Shai Ben-David, Dvid Pl, Shai Shalev-Shwartz |
| 2009 | COLT | The Complexity of Improperly Learning Large Margin Halfspaces. | Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan |
| 2009 | COLT | Stochastic Convex Optimization. | Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan |
| 2009 | COLT | Learnability and Stability in the General Learning Setting. | Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan |
| 2009 | ICML | Stochastic methods for | Shai Shalev-Shwartz, Ambuj Tewari |
| 2008 | COLT | On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms. | Shai Shalev-Shwartz, Yoram Singer |
| 2008 | ICML | Efficient projections onto the | John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Tushar Chandra |
| 2008 | ICML | Efficient bandit algorithms for online multiclass prediction. | Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari |
| 2008 | ICML | SVM optimization: inverse dependence on training set size. | Shai Shalev-Shwartz, Nathan Srebro |
| 2007 | COLT | Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking. | Sivan Sabato, Shai Shalev-Shwartz |
| 2007 | ICML | Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. | Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro |
| 2006 | COLT | Online Learning Meets Optimization in the Dual. | Shai Shalev-Shwartz, Yoram Singer |
| 2006 | ICML | Online multiclass learning by interclass hypothesis sharing. | Michael Fink, Shai Shalev-Shwartz, Yoram Singer, Shimon Ullman |
| 2006 | Interspeech | Discriminative kernel-based phoneme sequence recognition. | Joseph Keshet, Shai Shalev-Shwartz, Samy Bengio, Yoram Singer, Dan Chazan |
| 2005 | COLT | A New Perspective on an Old Perceptron Algorithm. | Shai Shalev-Shwartz, Yoram Singer |
| 2005 | Interspeech | Phoneme alignment based on discriminative learning. | Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer, Dan Chazan |
| 2004 | ICML | Online and batch learning of pseudo-metrics. | Shai Shalev-Shwartz, Yoram Singer, Andrew Y. Ng |
| 2003 | COLT | Smooth e-Intensive Regression by Loss Symmetrization. | Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer |
| 2002 | SIGIR | Robust temporal and spectral modeling for query By melody. | Shai Shalev-Shwartz, Shlomo Dubnov, Nir Friedman, Yoram Singer |