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Books and Theses
- 2010
[b1]Ohad Shamir:
On stability in statistical machine learning (עם תקציר בעברית ושער נוסף: על יציבות בלמידה חישובית סטטיסטית.). Hebrew University of Jerusalem, Israel, 2010
Journal Articles
- 2024
- [j20]Guy Kornowski, Ohad Shamir:
An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization. J. Mach. Learn. Res. 25: 122:1-122:14 (2024) - 2023
- [j19]Ohad Shamir:
The Implicit Bias of Benign Overfitting. J. Mach. Learn. Res. 24: 113:1-113:40 (2023) - 2022
- [j18]Guy Kornowski, Ohad Shamir:
Oracle Complexity in Nonsmooth Nonconvex Optimization. J. Mach. Learn. Res. 23: 314:1-314:44 (2022) - 2021
- [j17]Ohad Shamir:
Gradient Methods Never Overfit On Separable Data. J. Mach. Learn. Res. 22: 85:1-85:20 (2021) - 2019
- [j16]Yossi Arjevani, Ohad Shamir, Ron Shiff
:
Oracle complexity of second-order methods for smooth convex optimization. Math. Program. 178(1-2): 327-360 (2019) - 2018
- [j15]Ohad Shamir:
Distribution-Specific Hardness of Learning Neural Networks. J. Mach. Learn. Res. 19: 32:1-32:29 (2018) - 2017
- [j14]Ohad Shamir:
An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback. J. Mach. Learn. Res. 18: 52:1-52:11 (2017) - [j13]Noga Alon, Nicolò Cesa-Bianchi, Claudio Gentile, Shie Mannor, Yishay Mansour, Ohad Shamir:
Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback. SIAM J. Comput. 46(6): 1785-1826 (2017) - 2016
- [j12]Yossi Arjevani, Shai Shalev-Shwartz, Ohad Shamir:
On Lower and Upper Bounds in Smooth and Strongly Convex Optimization. J. Mach. Learn. Res. 17: 126:1-126:51 (2016) - [j11]Niv Buchbinder, Shahar Chen, Joseph Naor, Ohad Shamir:
Unified Algorithms for Online Learning and Competitive Analysis. Math. Oper. Res. 41(2): 612-625 (2016) - 2015
- [j10]Ohad Shamir:
The sample complexity of learning linear predictors with the squared loss. J. Mach. Learn. Res. 16: 3475-3486 (2015) - 2014
- [j9]Ohad Shamir, Shai Shalev-Shwartz:
Matrix completion with the trace norm: learning, bounding, and transducing. J. Mach. Learn. Res. 15(1): 3401-3423 (2014) - 2012
- [j8]Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao:
Optimal Distributed Online Prediction Using Mini-Batches. J. Mach. Learn. Res. 13: 165-202 (2012) - 2011
- [j7]Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir:
Efficient Learning with Partially Observed Attributes. J. Mach. Learn. Res. 12: 2857-2878 (2011) - [j6]Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan:
Learning Kernel-Based Halfspaces with the 0-1 Loss. SIAM J. Comput. 40(6): 1623-1646 (2011) - [j5]Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir:
Online Learning of Noisy Data. IEEE Trans. Inf. Theory 57(12): 7907-7931 (2011) - 2010
- [j4]Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Learnability, Stability and Uniform Convergence. J. Mach. Learn. Res. 11: 2635-2670 (2010) - [j3]Ohad Shamir, Naftali Tishby:
Stability and model selection in k-means clustering. Mach. Learn. 80(2-3): 213-243 (2010) - [j2]Ofer Dekel, Ohad Shamir, Lin Xiao:
Learning to classify with missing and corrupted features. Mach. Learn. 81(2): 149-178 (2010) - [j1]Ohad Shamir, Sivan Sabato, Naftali Tishby:
Learning and generalization with the information bottleneck. Theor. Comput. Sci. 411(29-30): 2696-2711 (2010)
Conference and Workshop Papers
- 2024
- [c112]Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir, Nathan Srebro:
Depth Separation in Norm-Bounded Infinite-Width Neural Networks. COLT 2024: 4082-4114 - [c111]Guy Kornowski, Ohad Shamir:
Open Problem: Anytime Convergence Rate of Gradient Descent. COLT 2024: 5335-5339 - [c110]Daniel Barzilai, Ohad Shamir:
Generalization in Kernel Regression Under Realistic Assumptions. ICML 2024 - 2023
- [c109]Nadav Timor, Gal Vardi, Ohad Shamir:
Implicit Regularization Towards Rank Minimization in ReLU Networks. ALT 2023: 1429-1459 - [c108]Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis:
Deterministic Nonsmooth Nonconvex Optimization. COLT 2023: 4570-4597 - [c107]Nikita Kornilov, Ohad Shamir, Aleksandr V. Lobanov, Darina Dvinskikh, Alexander V. Gasnikov, Innokentiy Shibaev, Eduard Gorbunov, Samuel Horváth:
Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance. NeurIPS 2023 - [c106]Guy Kornowski, Gilad Yehudai, Ohad Shamir:
From Tempered to Benign Overfitting in ReLU Neural Networks. NeurIPS 2023 - [c105]Roey Magen, Ohad Shamir:
Initialization-Dependent Sample Complexity of Linear Predictors and Neural Networks. NeurIPS 2023 - 2022
- [c104]Ohad Shamir:
The Implicit Bias of Benign Overfitting. COLT 2022: 448-478 - [c103]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Width is Less Important than Depth in ReLU Neural Networks. COLT 2022: 1249-1281 - [c102]Gal Vardi, Gilad Yehudai, Ohad Shamir:
On the Optimal Memorization Power of ReLU Neural Networks. ICLR 2022 - [c101]Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro:
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract). IJCAI 2022: 5359-5363 - [c100]Ohad Shamir:
Elephant in the Room: Non-Smooth Non-Convex Optimization. ISAIM 2022 - [c99]Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, Michal Irani:
Reconstructing Training Data From Trained Neural Networks. NeurIPS 2022 - [c98]Gal Vardi, Ohad Shamir, Nati Srebro:
The Sample Complexity of One-Hidden-Layer Neural Networks. NeurIPS 2022 - [c97]Gal Vardi, Ohad Shamir, Nati Srebro:
On Margin Maximization in Linear and ReLU Networks. NeurIPS 2022 - [c96]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Gradient Methods Provably Converge to Non-Robust Networks. NeurIPS 2022 - 2021
- [c95]Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir:
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks. COLT 2021: 3265-3295 - [c94]Itay Safran, Gilad Yehudai, Ohad Shamir:
The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks. COLT 2021: 3889-3934 - [c93]Gal Vardi, Daniel Reichman, Toniann Pitassi, Ohad Shamir:
Size and Depth Separation in Approximating Benign Functions with Neural Networks. COLT 2021: 4195-4223 - [c92]Gal Vardi, Ohad Shamir:
Implicit Regularization in ReLU Networks with the Square Loss. COLT 2021: 4224-4258 - [c91]Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro:
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. COLT 2021: 4386-4437 - [c90]Guy Kornowski, Ohad Shamir:
Oracle Complexity in Nonsmooth Nonconvex Optimization. NeurIPS 2021: 324-334 - [c89]Itay Safran, Ohad Shamir:
Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems. NeurIPS 2021: 15151-15161 - [c88]Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth:
A Stochastic Newton Algorithm for Distributed Convex Optimization. NeurIPS 2021: 26818-26830 - [c87]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Learning a Single Neuron with Bias Using Gradient Descent. NeurIPS 2021: 28690-28700 - 2020
- [c86]Yossi Arjevani, Ohad Shamir, Nathan Srebro:
A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates. ALT 2020: 111-132 - [c85]Itay Safran, Ohad Shamir:
How Good is SGD with Random Shuffling? COLT 2020: 3250-3284 - [c84]Gilad Yehudai, Ohad Shamir:
Learning a Single Neuron with Gradient Methods. COLT 2020: 3756-3786 - [c83]Yoel Drori, Ohad Shamir:
The Complexity of Finding Stationary Points with Stochastic Gradient Descent. ICML 2020: 2658-2667 - [c82]Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir:
Proving the Lottery Ticket Hypothesis: Pruning is All You Need. ICML 2020: 6682-6691 - [c81]Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro:
Is Local SGD Better than Minibatch SGD? ICML 2020: 10334-10343 - [c80]Gal Vardi, Ohad Shamir:
Neural Networks with Small Weights and Depth-Separation Barriers. NeurIPS 2020 - 2019
- [c79]Yuval Dagan, Gil Kur, Ohad Shamir:
Space lower bounds for linear prediction in the streaming model. COLT 2019: 929-954 - [c78]Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth:
The Complexity of Making the Gradient Small in Stochastic Convex Optimization. COLT 2019: 1319-1345 - [c77]Itay Safran, Ronen Eldan, Ohad Shamir:
Depth Separations in Neural Networks: What is Actually Being Separated? COLT 2019: 2664-2666 - [c76]Ohad Shamir:
Exponential Convergence Time of Gradient Descent for One-Dimensional Deep Linear Neural Networks. COLT 2019: 2691-2713 - [c75]Gilad Yehudai, Ohad Shamir:
On the Power and Limitations of Random Features for Understanding Neural Networks. NeurIPS 2019: 6594-6604 - 2018
- [c74]Nicolò Cesa-Bianchi, Ohad Shamir:
Bandit Regret Scaling with the Effective Loss Range. ALT 2018: 128-151 - [c73]Noah Golowich, Alexander Rakhlin, Ohad Shamir:
Size-Independent Sample Complexity of Neural Networks. COLT 2018: 297-299 - [c72]Yuval Dagan, Ohad Shamir:
Detecting Correlations with Little Memory and Communication. COLT 2018: 1145-1198 - [c71]Itay Safran, Ohad Shamir:
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks. ICML 2018: 4430-4438 - [c70]Ohad Shamir:
Are ResNets Provably Better than Linear Predictors? NeurIPS 2018: 505-514 - [c69]Murat A. Erdogdu, Lester Mackey, Ohad Shamir:
Global Non-convex Optimization with Discretized Diffusions. NeurIPS 2018: 9694-9703 - 2017
- [c68]Satyen Kale, Ohad Shamir:
Preface: Conference on Learning Theory (COLT), 2017. COLT 2017: 1-3 - [c67]Yossi Arjevani, Ohad Shamir:
Oracle Complexity of Second-Order Methods for Finite-Sum Problems. ICML 2017: 205-213 - [c66]Dan Garber, Ohad Shamir, Nathan Srebro:
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. ICML 2017: 1203-1212 - [c65]Itay Safran, Ohad Shamir:
Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks. ICML 2017: 2979-2987 - [c64]Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah:
Failures of Gradient-Based Deep Learning. ICML 2017: 3067-3075 - [c63]Ohad Shamir, Liran Szlak:
Online Learning with Local Permutations and Delayed Feedback. ICML 2017: 3086-3094 - 2016
- [c62]Ronen Eldan, Ohad Shamir:
The Power of Depth for Feedforward Neural Networks. COLT 2016: 907-940 - [c61]Jonathan Rosenski, Ohad Shamir, Liran Szlak:
Multi-Player Bandits - a Musical Chairs Approach. ICML 2016: 155-163 - [c60]Ohad Shamir:
Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity. ICML 2016: 248-256 - [c59]Ohad Shamir:
Convergence of Stochastic Gradient Descent for PCA. ICML 2016: 257-265 - [c58]Itay Safran, Ohad Shamir:
On the Quality of the Initial Basin in Overspecified Neural Networks. ICML 2016: 774-782 - [c57]Yossi Arjevani, Ohad Shamir:
On the Iteration Complexity of Oblivious First-Order Optimization Algorithms. ICML 2016: 908-916 - [c56]Ohad Shamir:
Without-Replacement Sampling for Stochastic Gradient Methods. NIPS 2016: 46-54 - [c55]Yossi Arjevani, Ohad Shamir:
Dimension-Free Iteration Complexity of Finite Sum Optimization Problems. NIPS 2016: 3540-3548 - 2015
- [c54]Ethan Fetaya, Ohad Shamir, Shimon Ullman:
Graph Approximation and Clustering on a Budget. AISTATS 2015 - [c53]Nicolò Cesa-Bianchi, Yishay Mansour, Ohad Shamir:
On the Complexity of Learning with Kernels. COLT 2015: 297-325 - [c52]Ohad Shamir:
On the Complexity of Bandit Linear Optimization. COLT 2015: 1523-1551 - [c51]Ohad Shamir:
A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate. ICML 2015: 144-152 - [c50]Doron Kukliansky, Ohad Shamir:
Attribute Efficient Linear Regression with Distribution-Dependent Sampling. ICML 2015: 153-161 - [c49]Yossi Arjevani, Ohad Shamir:
Communication Complexity of Distributed Convex Learning and Optimization. NIPS 2015: 1756-1764 - 2014
- [c48]Ohad Shamir, Nathan Srebro:
Distributed stochastic optimization and learning. Allerton 2014: 850-857 - [c47]Ishai Menache, Ohad Shamir, Navendu Jain:
On-demand, Spot, or Both: Dynamic Resource Allocation for Executing Batch Jobs in the Cloud. ICAC 2014: 177-187 - [c46]Ohad Shamir, Nathan Srebro, Tong Zhang:
Communication-Efficient Distributed Optimization using an Approximate Newton-type Method. ICML 2014: 1000-1008 - [c45]Ohad Shamir:
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation. NIPS 2014: 163-171 - [c44]Roi Livni, Shai Shalev-Shwartz, Ohad Shamir:
On the Computational Efficiency of Training Neural Networks. NIPS 2014: 855-863 - 2013
- [c43]Alexander Rakhlin, Ohad Shamir, Karthik Sridharan:
Localization and Adaptation in Online Learning. AISTATS 2013: 516-526 - [c42]Nicolò Cesa-Bianchi, Ohad Shamir:
Efficient Transductive Online Learning via Randomized Rounding. Empirical Inference 2013: 177-194 - [c41]Ohad Shamir:
On the Complexity of Bandit and Derivative-Free Stochastic Convex Optimization. COLT 2013: 3-24 - [c40]Oren Anava, Elad Hazan, Shie Mannor, Ohad Shamir:
Online Learning for Time Series Prediction. COLT 2013: 172-184 - [c39]Baoyuan Liu, Fereshteh Sadeghi, Marshall F. Tappen, Ohad Shamir, Ce Liu:
Probabilistic Label Trees for Efficient Large Scale Image Classification. CVPR 2013: 843-850 - [c38]Ohad Shamir, Tong Zhang:
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes. ICML (1) 2013: 71-79 - [c37]Nicolò Cesa-Bianchi, Ofer Dekel, Ohad Shamir:
Online Learning with Switching Costs and Other Adaptive Adversaries. NIPS 2013: 1160-1168 - [c36]Or Zuk, Amnon Amir, Amit Zeisel, Ohad Shamir, Noam Shental:
Accurate Profiling of Microbial Communities from Massively Parallel Sequencing Using Convex Optimization. SPIRE 2013: 279-297 - 2012
- [c35]Orly Avner, Shie Mannor, Ohad Shamir:
Decoupling Exploration and Exploitation in Multi-Armed Bandits. ICML 2012 - [c34]Alexander Rakhlin, Ohad Shamir, Karthik Sridharan:
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization. ICML 2012 - [c33]Alexander Rakhlin, Ohad Shamir, Karthik Sridharan:
Relax and Randomize : From Value to Algorithms. NIPS 2012: 2150-2158 - [c32]Niv Buchbinder, Shahar Chen, Joseph Naor, Ohad Shamir:
Unified Algorithms for Online Learning and Competitive Analysis. COLT 2012: 5.1-5.18 - [c31]Ohad Shamir:
Open Problem: Is Averaging Needed for Strongly Convex Stochastic Gradient Descent? COLT 2012: 47.1-47.3 - [c30]Ofer Dekel, Ohad Shamir:
There's a Hole in My Data Space: Piecewise Predictors for Heterogeneous Learning Problems. AISTATS 2012: 291-298 - [c29]Shai Shalev-Shwartz, Ohad Shamir, Eran Tromer:
Using More Data to Speed-up Training Time. AISTATS 2012: 1019-1027 - [c28]Ruth Urner, Shai Ben-David, Ohad Shamir:
Learning from Weak Teachers. AISTATS 2012: 1252-1260 - 2011
- [c27]Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir:
Quantity Makes Quality: Learning with Partial Views. AAAI 2011: 1547-1550 - [c26]Shai Shalev-Shwartz, Alon Gonen, Ohad Shamir:
Large-Scale Convex Minimization with a Low-Rank Constraint. ICML 2011: 329-336 - [c25]Omer Tamuz, Ce Liu, Serge J. Belongie, Ohad Shamir, Adam Kalai:
Adaptively Learning the Crowd Kernel. ICML 2011: 673-680 - [c24]Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao:
Optimal Distributed Online Prediction. ICML 2011: 713-720 - [c23]Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan:
Learning Linear and Kernel Predictors with the 0-1 Loss Function. IJCAI 2011: 2740-2745 - [c22]Nicolò Cesa-Bianchi, Ohad Shamir:
Efficient Online Learning via Randomized Rounding. NIPS 2011: 343-351 - [c21]Shie Mannor, Ohad Shamir:
From Bandits to Experts: On the Value of Side-Observations. NIPS 2011: 684-692 - [c20]Sham M. Kakade, Adam Kalai, Varun Kanade, Ohad Shamir:
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression. NIPS 2011: 927-935 - [c19]Andrew Cotter, Ohad Shamir, Nati Srebro, Karthik Sridharan:
Better Mini-Batch Algorithms via Accelerated Gradient Methods. NIPS 2011: 1647-1655 - [c18]Rina Foygel, Ruslan Salakhutdinov, Ohad Shamir, Nati Srebro:
Learning with the weighted trace-norm under arbitrary sampling distributions. NIPS 2011: 2133-2141 - [c17]Ohad Shamir, Shai Shalev-Shwartz:
Collaborative Filtering with the Trace Norm: Learning, Bounding, and Transducing. COLT 2011: 661-678 - [c16]Ohad Shamir, Naftali Tishby:
Spectral Clustering on a Budget. AISTATS 2011: 661-669 - 2010
- [c15]Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir:
Online Learning of Noisy Data with Kernels. COLT 2010: 218-230 - [c14]Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan:
Learning Kernel-Based Halfspaces with the Zero-One Loss. COLT 2010: 441-450 - [c13]Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir:
Efficient Learning with Partially Observed Attributes. ICML 2010: 183-190 - [c12]Ofer Dekel, Ohad Shamir:
Multiclass-Multilabel Classification with More Classes than Examples. AISTATS 2010: 137-144 - [c11]Sham M. Kakade, Ohad Shamir, Karthik Sindharan, Ambuj Tewari:
Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity. AISTATS 2010: 381-388 - 2009
- [c10]Ofer Dekel, Ohad Shamir:
Vox Populi: Collecting High-Quality Labels from a Crowd. COLT 2009 - [c9]Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan:
The Complexity of Improperly Learning Large Margin Halfspaces. COLT 2009 - [c8]Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Stochastic Convex Optimization. COLT 2009 - [c7]Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Learnability and Stability in the General Learning Setting. COLT 2009 - [c6]Ofer Dekel, Ohad Shamir:
Good learners for evil teachers. ICML 2009: 233-240 - 2008
- [c5]Ohad Shamir, Sivan Sabato, Naftali Tishby:
Learning and Generalization with the Information Bottleneck. ALT 2008: 92-107 - [c4]Ohad Shamir, Naftali Tishby:
Model Selection and Stability in k-means Clustering. COLT 2008: 367-378 - [c3]Ofer Dekel, Ohad Shamir:
Learning to classify with missing and corrupted features. ICML 2008: 216-223 - [c2]Ohad Shamir, Naftali Tishby:
On the Reliability of Clustering Stability in the Large Sample Regime. NIPS 2008: 1465-1472 - 2007
- [c1]Ohad Shamir, Naftali Tishby:
Cluster Stability for Finite Samples. NIPS 2007: 1297-1304
Editorship
- 2017
- [e2]Satyen Kale, Ohad Shamir:
Proceedings of the 30th Conference on Learning Theory, COLT 2017, Amsterdam, The Netherlands, 7-10 July 2017. Proceedings of Machine Learning Research 65, PMLR 2017 [contents] - 2016
- [e1]Vitaly Feldman, Alexander Rakhlin, Ohad Shamir:
Proceedings of the 29th Conference on Learning Theory, COLT 2016, New York, USA, June 23-26, 2016. JMLR Workshop and Conference Proceedings 49, JMLR.org 2016 [contents]
Informal and Other Publications
- 2024
- [i103]Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir, Nathan Srebro:
Depth Separation in Norm-Bounded Infinite-Width Neural Networks. CoRR abs/2402.08808 (2024) - [i102]Guy Kornowski, Ohad Shamir:
Open Problem: Anytime Convergence Rate of Gradient Descent. CoRR abs/2406.13888 (2024) - [i101]Guy Kornowski, Swati Padmanabhan, Ohad Shamir:
On the Hardness of Meaningful Local Guarantees in Nonsmooth Nonconvex Optimization. CoRR abs/2409.10323 (2024) - [i100]Daniel Barzilai, Ohad Shamir:
Simple Relative Deviation Bounds for Covariance and Gram Matrices. CoRR abs/2410.05754 (2024) - 2023
- [i99]Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis:
Deterministic Nonsmooth Nonconvex Optimization. CoRR abs/2302.08300 (2023) - [i98]Guy Kornowski, Gilad Yehudai, Ohad Shamir:
From Tempered to Benign Overfitting in ReLU Neural Networks. CoRR abs/2305.15141 (2023) - [i97]Roey Magen, Ohad Shamir:
Initialization-Dependent Sample Complexity of Linear Predictors and Neural Networks. CoRR abs/2305.16475 (2023) - [i96]Guy Kornowski, Ohad Shamir:
An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization. CoRR abs/2307.04504 (2023) - [i95]Daniel Barzilai, Ohad Shamir:
Generalization in Kernel Regression Under Realistic Assumptions. CoRR abs/2312.15995 (2023) - 2022
- [i94]Ohad Shamir:
The Implicit Bias of Benign Overfitting. CoRR abs/2201.11489 (2022) - [i93]Nadav Timor, Gal Vardi, Ohad Shamir:
Implicit Regularization Towards Rank Minimization in ReLU Networks. CoRR abs/2201.12760 (2022) - [i92]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Width is Less Important than Depth in ReLU Neural Networks. CoRR abs/2202.03841 (2022) - [i91]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Gradient Methods Provably Converge to Non-Robust Networks. CoRR abs/2202.04347 (2022) - [i90]Gal Vardi, Ohad Shamir, Nathan Srebro:
The Sample Complexity of One-Hidden-Layer Neural Networks. CoRR abs/2202.06233 (2022) - [i89]Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, Michal Irani:
Reconstructing Training Data from Trained Neural Networks. CoRR abs/2206.07758 (2022) - [i88]Guy Kornowski, Ohad Shamir:
On the Complexity of Finding Small Subgradients in Nonsmooth Optimization. CoRR abs/2209.10346 (2022) - 2021
- [i87]Gal Vardi, Daniel Reichman, Toniann Pitassi, Ohad Shamir:
Size and Depth Separation in Approximating Natural Functions with Neural Networks. CoRR abs/2102.00314 (2021) - [i86]Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir:
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks. CoRR abs/2102.00434 (2021) - [i85]Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro:
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. CoRR abs/2102.01583 (2021) - [i84]Guy Kornowski, Ohad Shamir:
Oracle Complexity in Nonsmooth Nonconvex Optimization. CoRR abs/2104.06763 (2021) - [i83]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Learning a Single Neuron with Bias Using Gradient Descent. CoRR abs/2106.01101 (2021) - [i82]Itay Safran, Ohad Shamir:
Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems. CoRR abs/2106.06880 (2021) - [i81]Gal Vardi, Ohad Shamir, Nathan Srebro:
On Margin Maximization in Linear and ReLU Networks. CoRR abs/2110.02732 (2021) - [i80]Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth:
A Stochastic Newton Algorithm for Distributed Convex Optimization. CoRR abs/2110.02954 (2021) - [i79]Gal Vardi, Gilad Yehudai, Ohad Shamir:
On the Optimal Memorization Power of ReLU Neural Networks. CoRR abs/2110.03187 (2021) - [i78]Liran Szlak, Ohad Shamir:
Convergence Results For Q-Learning With Experience Replay. CoRR abs/2112.04213 (2021) - [i77]Liran Szlak, Ohad Shamir:
Replay For Safety. CoRR abs/2112.04229 (2021) - 2020
- [i76]Gilad Yehudai, Ohad Shamir:
Learning a Single Neuron with Gradient Methods. CoRR abs/2001.05205 (2020) - [i75]Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir:
Proving the Lottery Ticket Hypothesis: Pruning is All You Need. CoRR abs/2002.00585 (2020) - [i74]Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro:
Is Local SGD Better than Minibatch SGD? CoRR abs/2002.07839 (2020) - [i73]Ohad Shamir:
Can We Find Near-Approximately-Stationary Points of Nonsmooth Nonconvex Functions? CoRR abs/2002.11962 (2020) - [i72]Gal Vardi, Ohad Shamir:
Neural Networks with Small Weights and Depth-Separation Barriers. CoRR abs/2006.00625 (2020) - [i71]Itay Safran, Gilad Yehudai, Ohad Shamir:
The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks. CoRR abs/2006.01005 (2020) - [i70]Ohad Shamir:
Gradient Methods Never Overfit On Separable Data. CoRR abs/2007.00028 (2020) - [i69]Guy Kornowski, Ohad Shamir:
High-Order Oracle Complexity of Smooth and Strongly Convex Optimization. CoRR abs/2010.06642 (2020) - [i68]Gal Vardi, Ohad Shamir:
Implicit Regularization in ReLU Networks with the Square Loss. CoRR abs/2012.05156 (2020) - [i67]Gal Vardi, Ohad Shamir:
Neural Networks with Small Weights and Depth-Separation Barriers. Electron. Colloquium Comput. Complex. TR20 (2020) - 2019
- [i66]Yuval Dagan, Gil Kur, Ohad Shamir:
Space lower bounds for linear prediction. CoRR abs/1902.03498 (2019) - [i65]Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth:
The Complexity of Making the Gradient Small in Stochastic Convex Optimization. CoRR abs/1902.04686 (2019) - [i64]Gilad Yehudai, Ohad Shamir:
On the Power and Limitations of Random Features for Understanding Neural Networks. CoRR abs/1904.00687 (2019) - [i63]Itay Safran, Ronen Eldan, Ohad Shamir:
Depth Separations in Neural Networks: What is Actually Being Separated? CoRR abs/1904.06984 (2019) - [i62]Itay Safran, Ohad Shamir:
How Good is SGD with Random Shuffling? CoRR abs/1908.00045 (2019) - [i61]Yoel Drori, Ohad Shamir:
The Complexity of Finding Stationary Points with Stochastic Gradient Descent. CoRR abs/1910.01845 (2019) - 2018
- [i60]Yuval Dagan, Ohad Shamir:
Detecting Correlations with Little Memory and Communication. CoRR abs/1803.01420 (2018) - [i59]Ohad Shamir:
Are ResNets Provably Better than Linear Predictors? CoRR abs/1804.06739 (2018) - [i58]Yossi Arjevani, Ohad Shamir, Nathan Srebro:
A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates. CoRR abs/1806.10188 (2018) - [i57]Ohad Shamir:
Exponential Convergence Time of Gradient Descent for One-Dimensional Deep Linear Neural Networks. CoRR abs/1809.08587 (2018) - [i56]Murat A. Erdogdu, Lester Mackey, Ohad Shamir:
Global Non-convex Optimization with Discretized Diffusions. CoRR abs/1810.12361 (2018) - 2017
- [i55]Dan Garber, Ohad Shamir, Nathan Srebro:
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. CoRR abs/1702.08169 (2017) - [i54]Ohad Shamir, Liran Szlak:
Online Learning with Local Permutations and Delayed Feedback. CoRR abs/1703.04274 (2017) - [i53]Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah:
Failures of Deep Learning. CoRR abs/1703.07950 (2017) - [i52]Nicolò Cesa-Bianchi, Ohad Shamir:
Bandit Regret Scaling with the Effective Loss Range. CoRR abs/1705.05091 (2017) - [i51]Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah:
Weight Sharing is Crucial to Succesful Optimization. CoRR abs/1706.00687 (2017) - [i50]Noah Golowich, Alexander Rakhlin, Ohad Shamir:
Size-Independent Sample Complexity of Neural Networks. CoRR abs/1712.06541 (2017) - [i49]Itay Safran, Ohad Shamir:
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks. CoRR abs/1712.08968 (2017) - 2016
- [i48]Ohad Shamir:
Without-Replacement Sampling for Stochastic Gradient Methods: Convergence Results and Application to Distributed Optimization. CoRR abs/1603.00570 (2016) - [i47]Yossi Arjevani, Ohad Shamir:
On the Iteration Complexity of Oblivious First-Order Optimization Algorithms. CoRR abs/1605.03529 (2016) - [i46]Yossi Arjevani, Ohad Shamir:
Dimension-Free Iteration Complexity of Finite Sum Optimization Problems. CoRR abs/1606.09333 (2016) - [i45]Ohad Shamir:
Distribution-Specific Hardness of Learning Neural Networks. CoRR abs/1609.01037 (2016) - [i44]Itay Safran, Ohad Shamir:
Depth Separation in ReLU Networks for Approximating Smooth Non-Linear Functions. CoRR abs/1610.09887 (2016) - [i43]Yossi Arjevani, Ohad Shamir:
Oracle Complexity of Second-Order Methods for Finite-Sum Problems. CoRR abs/1611.04982 (2016) - 2015
- [i42]Yossi Arjevani, Shai Shalev-Shwartz, Ohad Shamir:
On Lower and Upper Bounds for Smooth and Strongly Convex Optimization Problems. CoRR abs/1503.06833 (2015) - [i41]Yossi Arjevani, Ohad Shamir:
Communication Complexity of Distributed Convex Learning and Optimization. CoRR abs/1506.01900 (2015) - [i40]Ohad Shamir:
An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback. CoRR abs/1507.08752 (2015) - [i39]Ohad Shamir:
Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity. CoRR abs/1507.08788 (2015) - [i38]Ohad Shamir:
Convergence of Stochastic Gradient Descent for PCA. CoRR abs/1509.09002 (2015) - [i37]Itay Safran, Ohad Shamir:
On the Quality of the Initial Basin in Overspecified Neural Networks. CoRR abs/1511.04210 (2015) - [i36]Jonathan Rosenski, Ohad Shamir, Liran Szlak:
Multi-Player Bandits - a Musical Chairs Approach. CoRR abs/1512.02866 (2015) - [i35]Ronen Eldan, Ohad Shamir:
The Power of Depth for Feedforward Neural Networks. CoRR abs/1512.03965 (2015) - 2014
- [i34]Ethan Fetaya, Ohad Shamir, Shimon Ullman:
Graph Approximation and Clustering on a Budget. CoRR abs/1406.2602 (2014) - [i33]Ohad Shamir:
The Sample Complexity of Learning Linear Predictors with the Squared Loss. CoRR abs/1406.5143 (2014) - [i32]Ohad Shamir:
On the Complexity of Bandit Linear Optimization. CoRR abs/1408.2368 (2014) - [i31]Ohad Shamir:
A Stochastic PCA Algorithm with an Exponential Convergence Rate. CoRR abs/1409.2848 (2014) - [i30]Noga Alon, Nicolò Cesa-Bianchi, Claudio Gentile, Shie Mannor, Yishay Mansour, Ohad Shamir:
Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback. CoRR abs/1409.8428 (2014) - [i29]Roi Livni, Shai Shalev-Shwartz, Ohad Shamir:
On the Computational Efficiency of Training Neural Networks. CoRR abs/1410.1141 (2014) - [i28]Doron Kukliansky, Ohad Shamir:
Attribute Efficient Linear Regression with Data-Dependent Sampling. CoRR abs/1410.6382 (2014) - [i27]Nicolò Cesa-Bianchi, Yishay Mansour, Ohad Shamir:
On the Complexity of Learning with Kernels. CoRR abs/1411.1158 (2014) - 2013
- [i26]Nicolò Cesa-Bianchi, Ofer Dekel, Ohad Shamir:
Online Learning with Switching Costs and Other Adaptive Adversaries. CoRR abs/1302.4387 (2013) - [i25]Oren Anava, Elad Hazan, Shie Mannor, Ohad Shamir:
Online Learning for Time Series Prediction. CoRR abs/1302.6927 (2013) - [i24]Roi Livni, Shai Shalev-Shwartz, Ohad Shamir:
A Provably Efficient Algorithm for Training Deep Networks. CoRR abs/1304.7045 (2013) - [i23]Or Zuk, Amnon Amir, Amit Zeisel, Ohad Shamir, Noam Shental:
Accurate Profiling of Microbial Communities from Massively Parallel Sequencing using Convex Optimization. CoRR abs/1309.6919 (2013) - [i22]Ohad Shamir:
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation. CoRR abs/1311.3494 (2013) - [i21]Ohad Shamir, Nathan Srebro, Tong Zhang:
Communication Efficient Distributed Optimization using an Approximate Newton-type Method. CoRR abs/1312.7853 (2013) - 2012
- [i20]Alexander Rakhlin, Ohad Shamir, Karthik Sridharan:
Relax and Localize: From Value to Algorithms. CoRR abs/1204.0870 (2012) - [i19]Orly Avner, Shie Mannor, Ohad Shamir:
Decoupling Exploration and Exploitation in Multi-Armed Bandits. CoRR abs/1205.2874 (2012) - [i18]Ohad Shamir:
On the Complexity of Bandit and Derivative-Free Stochastic Convex Optimization. CoRR abs/1209.2388 (2012) - [i17]Ohad Shamir, Tong Zhang:
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes. CoRR abs/1212.1824 (2012) - 2011
- [i16]Sham M. Kakade, Adam Tauman Kalai, Varun Kanade, Ohad Shamir:
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression. CoRR abs/1104.2018 (2011) - [i15]Omer Tamuz, Ce Liu, Serge J. Belongie, Ohad Shamir, Adam Tauman Kalai:
Adaptively Learning the Crowd Kernel. CoRR abs/1105.1033 (2011) - [i14]Shai Shalev-Shwartz, Ohad Shamir, Eran Tromer:
Using More Data to Speed-up Training Time. CoRR abs/1106.1216 (2011) - [i13]Shai Shalev-Shwartz, Alon Gonen, Ohad Shamir:
Large-Scale Convex Minimization with a Low-Rank Constraint. CoRR abs/1106.1622 (2011) - [i12]Nicolò Cesa-Bianchi, Ohad Shamir:
Efficient Online Learning via Randomized Rounding. CoRR abs/1106.2429 (2011) - [i11]Shie Mannor, Ohad Shamir:
From Bandits to Experts: On the Value of Side-Observations. CoRR abs/1106.2436 (2011) - [i10]Rina Foygel, Ruslan Salakhutdinov, Ohad Shamir, Nathan Srebro:
Learning with the Weighted Trace-norm under Arbitrary Sampling Distributions. CoRR abs/1106.4251 (2011) - [i9]Andrew Cotter, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Better Mini-Batch Algorithms via Accelerated Gradient Methods. CoRR abs/1106.4574 (2011) - [i8]Ohad Shamir:
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization. CoRR abs/1109.5647 (2011) - [i7]Ohad Shamir:
A Variant of Azuma's Inequality for Martingales with Subgaussian Tails. CoRR abs/1110.2392 (2011) - 2010
- [i6]Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir:
Efficient Learning with Partially Observed Attributes. CoRR abs/1004.4421 (2010) - [i5]Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir:
Online Learning of Noisy Data with Kernels. CoRR abs/1005.2296 (2010) - [i4]Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan:
Learning Kernel-Based Halfspaces with the Zero-One Loss. CoRR abs/1005.3681 (2010) - [i3]Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao:
Optimal Distributed Online Prediction using Mini-Batches. CoRR abs/1012.1367 (2010) - [i2]Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao:
Robust Distributed Online Prediction. CoRR abs/1012.1370 (2010) - 2009
- [i1]Sham M. Kakade, Ohad Shamir, Karthik Sridharan, Ambuj Tewari:
Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity. CoRR abs/0911.0054 (2009)
Coauthor Index
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