| 2026 | COLT | Limitations of SGD for Multi-Index Models Beyond Statistical Queries. | Daniel Barzilai, Ohad Shamir |
| 2025 | COLT | Logarithmic Width Suffices for Robust Memorization. | Amitsour Egosi, Gilad Yehudai, Ohad Shamir |
| 2025 | COLT | The Oracle Complexity of Simplex-based Matrix Games: Linear Separability and Nash Equilibria. | Guy Kornowski, Ohad Shamir |
| 2024 | COLT | Open Problem: Anytime Convergence Rate of Gradient Descent. | Guy Kornowski, Ohad Shamir |
| 2024 | COLT | Depth Separation in Norm-Bounded Infinite-Width Neural Networks. | Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir, Nathan Srebro |
| 2024 | ICML | Generalization in Kernel Regression Under Realistic Assumptions. | Daniel Barzilai, Ohad Shamir |
| 2023 | ALT | Implicit Regularization Towards Rank Minimization in ReLU Networks. | Nadav Timor, Gal Vardi, Ohad Shamir |
| 2023 | COLT | Deterministic Nonsmooth Nonconvex Optimization. | Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis |
| 2022 | COLT | The Implicit Bias of Benign Overfitting. | Ohad Shamir |
| 2022 | COLT | Width is Less Important than Depth in ReLU Neural Networks. | Gal Vardi, Gilad Yehudai, Ohad Shamir |
| 2022 | ICLR | On the Optimal Memorization Power of ReLU Neural Networks. | Gal Vardi, Gilad Yehudai, Ohad Shamir |
| 2022 | IJCAI | The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract). | Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro |
| 2022 | ISAIM | Elephant in the Room: Non-Smooth Non-Convex Optimization. | Ohad Shamir |
| 2021 | COLT | The Connection Between Approximation, Depth Separation and Learnability in Neural Networks. | Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir |
| 2021 | COLT | The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks. | Itay Safran, Gilad Yehudai, Ohad Shamir |
| 2021 | COLT | Size and Depth Separation in Approximating Benign Functions with Neural Networks. | Gal Vardi, Daniel Reichman, Toniann Pitassi, Ohad Shamir |
| 2021 | COLT | Implicit Regularization in ReLU Networks with the Square Loss. | Gal Vardi, Ohad Shamir |
| 2021 | COLT | The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. | Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro |
| 2020 | ALT | A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates. | Yossi Arjevani, Ohad Shamir, Nathan Srebro |
| 2020 | COLT | How Good is SGD with Random Shuffling? | Itay Safran, Ohad Shamir |
| 2020 | COLT | Learning a Single Neuron with Gradient Methods. | Gilad Yehudai, Ohad Shamir |
| 2020 | ICML | The Complexity of Finding Stationary Points with Stochastic Gradient Descent. | Yoel Drori, Ohad Shamir |
| 2020 | ICML | Proving the Lottery Ticket Hypothesis: Pruning is All You Need. | Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir |
| 2020 | ICML | Is Local SGD Better than Minibatch SGD? | Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro |
| 2019 | COLT | Space lower bounds for linear prediction in the streaming model. | Yuval Dagan, Gil Kur, Ohad Shamir |
| 2019 | COLT | The Complexity of Making the Gradient Small in Stochastic Convex Optimization. | Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth |
| 2019 | COLT | Depth Separations in Neural Networks: What is Actually Being Separated? | Itay Safran, Ronen Eldan, Ohad Shamir |
| 2019 | COLT | Exponential Convergence Time of Gradient Descent for One-Dimensional Deep Linear Neural Networks. | Ohad Shamir |
| 2018 | ALT | Bandit Regret Scaling with the Effective Loss Range. | Nicol Cesa-Bianchi, Ohad Shamir |
| 2018 | COLT | Detecting Correlations with Little Memory and Communication. | Yuval Dagan, Ohad Shamir |
| 2018 | COLT | Size-Independent Sample Complexity of Neural Networks. | Noah Golowich, Alexander Rakhlin, Ohad Shamir |
| 2018 | ICML | Spurious Local Minima are Common in Two-Layer ReLU Neural Networks. | Itay Safran, Ohad Shamir |
| 2017 | COLT | Preface: Conference on Learning Theory (COLT), 2017. | Satyen Kale, Ohad Shamir |
| 2017 | ICML | Oracle Complexity of Second-Order Methods for Finite-Sum Problems. | Yossi Arjevani, Ohad Shamir |
| 2017 | ICML | Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. | Dan Garber, Ohad Shamir, Nathan Srebro |
| 2017 | ICML | Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks. | Itay Safran, Ohad Shamir |
| 2017 | ICML | Failures of Gradient-Based Deep Learning. | Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah |
| 2017 | ICML | Online Learning with Local Permutations and Delayed Feedback. | Ohad Shamir, Liran Szlak |
| 2016 | COLT | The Power of Depth for Feedforward Neural Networks. | Ronen Eldan, Ohad Shamir |
| 2016 | ICML | On the Iteration Complexity of Oblivious First-Order Optimization Algorithms. | Yossi Arjevani, Ohad Shamir |
| 2016 | ICML | Multi-Player Bandits - a Musical Chairs Approach. | Jonathan Rosenski, Ohad Shamir, Liran Szlak |
| 2016 | ICML | On the Quality of the Initial Basin in Overspecified Neural Networks. | Itay Safran, Ohad Shamir |
| 2016 | ICML | Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity. | Ohad Shamir |
| 2016 | ICML | Convergence of Stochastic Gradient Descent for PCA. | Ohad Shamir |
| 2015 | AISTATS | Graph Approximation and Clustering on a Budget. | Ethan Fetaya, Ohad Shamir, Shimon Ullman |
| 2015 | COLT | On the Complexity of Learning with Kernels. | Nicol Cesa-Bianchi, Yishay Mansour, Ohad Shamir |
| 2015 | COLT | On the Complexity of Bandit Linear Optimization. | Ohad Shamir |
| 2015 | ICML | Attribute Efficient Linear Regression with Distribution-Dependent Sampling. | Doron Kukliansky, Ohad Shamir |
| 2015 | ICML | A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate. | Ohad Shamir |
| 2014 | ICML | Communication-Efficient Distributed Optimization using an Approximate Newton-type Method. | Ohad Shamir, Nathan Srebro, Tong Zhang |
| 2013 | AISTATS | Localization and Adaptation in Online Learning. | Alexander Rakhlin, Ohad Shamir, Karthik Sridharan |
| 2013 | COLT | Online Learning for Time Series Prediction. | Oren Anava, Elad Hazan, Shie Mannor, Ohad Shamir |
| 2013 | COLT | On the Complexity of Bandit and Derivative-Free Stochastic Convex Optimization. | Ohad Shamir |
| 2013 | CVPR | Probabilistic Label Trees for Efficient Large Scale Image Classification. | Baoyuan Liu, Fereshteh Sadeghi, Marshall F. Tappen, Ohad Shamir, Ce Liu |
| 2013 | ICML | Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes. | Ohad Shamir, Tong Zhang |
| 2013 | SPIRE | Accurate Profiling of Microbial Communities from Massively Parallel Sequencing Using Convex Optimization. | Or Zuk, Amnon Amir, Amit Zeisel, Ohad Shamir, Noam Shental |
| 2012 | ICML | Decoupling Exploration and Exploitation in Multi-Armed Bandits. | Orly Avner, Shie Mannor, Ohad Shamir |
| 2012 | ICML | Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization. | Alexander Rakhlin, Ohad Shamir, Karthik Sridharan |
| 2011 | AAAI | Quantity Makes Quality: Learning with Partial Views. | Nicol Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir |
| 2011 | ICML | Optimal Distributed Online Prediction. | Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao |
| 2011 | ICML | Large-Scale Convex Minimization with a Low-Rank Constraint. | Shai Shalev-Shwartz, Alon Gonen, Ohad Shamir |
| 2011 | ICML | Adaptively Learning the Crowd Kernel. | Omer Tamuz, Ce Liu, Serge J. Belongie, Ohad Shamir, Adam Kalai |
| 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 | 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 | Vox Populi: Collecting High-Quality Labels from a Crowd. | Ofer Dekel, Ohad Shamir |
| 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 | Good learners for evil teachers. | Ofer Dekel, Ohad Shamir |
| 2008 | ALT | Learning and Generalization with the Information Bottleneck. | Ohad Shamir, Sivan Sabato, Naftali Tishby |
| 2008 | COLT | Model Selection and Stability in k-means Clustering. | Ohad Shamir, Naftali Tishby |
| 2008 | ICML | Learning to classify with missing and corrupted features. | Ofer Dekel, Ohad Shamir |