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The Connection Between Approximation, Depth Separation and Learnability in Neural Networks


Jan 31, 2021
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir


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Computational Separation Between Convolutional and Fully-Connected Networks


Oct 03, 2020
Eran Malach, Shai Shalev-Shwartz


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When Hardness of Approximation Meets Hardness of Learning


Aug 23, 2020
Eran Malach, Shai Shalev-Shwartz


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On the Ethics of Building AI in a Responsible Manner


Mar 30, 2020
Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua


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Proving the Lottery Ticket Hypothesis: Pruning is All You Need


Feb 03, 2020
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir


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Learning Boolean Circuits with Neural Networks


Oct 25, 2019
Eran Malach, Shai Shalev-Shwartz


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The Implicit Bias of Depth: How Incremental Learning Drives Generalization


Sep 26, 2019
Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely

* 24 pages, 7 figures 

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SenseBERT: Driving Some Sense into BERT


Aug 15, 2019
Yoav Levine, Barak Lenz, Or Dagan, Dan Padnos, Or Sharir, Shai Shalev-Shwartz, Amnon Shashua, Yoav Shoham


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Discriminative Active Learning


Jul 15, 2019
Daniel Gissin, Shai Shalev-Shwartz

* 11 pages, 3 figures 

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Decoupling Gating from Linearity


Jun 12, 2019
Jonathan Fiat, Eran Malach, Shai Shalev-Shwartz


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Is Deeper Better only when Shallow is Good?


Mar 08, 2019
Eran Malach, Shai Shalev-Shwartz


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Vision Zero: on a Provable Method for Eliminating Roadway Accidents without Compromising Traffic Throughput


Jan 17, 2019
Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua


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On a Formal Model of Safe and Scalable Self-driving Cars


Oct 27, 2018
Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua


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A Provably Correct Algorithm for Deep Learning that Actually Works


Jun 24, 2018
Eran Malach, Shai Shalev-Shwartz


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Decoupling "when to update" from "how to update"


Mar 26, 2018
Eran Malach, Shai Shalev-Shwartz


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SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data


Oct 27, 2017
Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz


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Fast Rates for Empirical Risk Minimization of Strict Saddle Problems


Jun 04, 2017
Alon Gonen, Shai Shalev-Shwartz


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Weight Sharing is Crucial to Succesful Optimization


Jun 02, 2017
Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah


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Failures of Gradient-Based Deep Learning


Apr 26, 2017
Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah


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Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization


Apr 16, 2017
Alon Gonen, Shai Shalev-Shwartz


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SelfieBoost: A Boosting Algorithm for Deep Learning


Apr 08, 2017
Shai Shalev-Shwartz


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Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving


Oct 11, 2016
Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua


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Subspace Learning with Partial Information


May 26, 2016
Alon Gonen, Dan Rosenbaum, Yonina Eldar, Shai Shalev-Shwartz


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Solving Ridge Regression using Sketched Preconditioned SVRG


May 26, 2016
Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz


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Learning a Metric Embedding for Face Recognition using the Multibatch Method


May 24, 2016
Oren Tadmor, Yonatan Wexler, Tal Rosenwein, Shai Shalev-Shwartz, Amnon Shashua


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Minimizing the Maximal Loss: How and Why?


May 22, 2016
Shai Shalev-Shwartz, Yonatan Wexler

* ICML 2016 

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SDCA without Duality, Regularization, and Individual Convexity


May 21, 2016
Shai Shalev-Shwartz

* ICML 2016 

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On the Sample Complexity of End-to-end Training vs. Semantic Abstraction Training


Apr 23, 2016
Shai Shalev-Shwartz, Amnon Shashua


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Learning Sparse Low-Threshold Linear Classifiers


Apr 18, 2016
Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel Hsu, Tong Zhang

* Journal of Machine Learning Research, 16(Jul):1275-1304, 2015 

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