Shai Shalev-Shwartz

Publication Activity (10 Years)

Years Active: 2002-2024
Publications (10 Years): 63

Top Venues

CoRR

ICML

J. Mach. Learn. Res.

COLT

Publications

Opher Lieber, Barak Lenz, Hofit Bata, Gal Cohen, Jhonathan Osin, Itay Dalmedigos, Erez Safahi, Shaked Meirom, Yonatan Belinkov, Shai Shalev-Shwartz, Omri Abend, Raz Alon, Tomer Asida, Amir Bergman, Roman Glozman, Michael Gokhman, Avashalom Manevich, Nir Ratner, Noam Rozen, Erez Shwartz, Mor Zusman, Yoav Shoham
Jamba: A Hybrid Transformer-Mamba Language Model. CoRR (2024)
Kai-Chia Mo, Shai Shalev-Shwartz, Nisæl Shártov
Untangling Lariats: Subgradient Following of Variationally Penalized Objectives. CoRR (2024)
Yoshua Bengio, Geoffrey E. Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, Gillian K. Hadfield, Jeff Clune, Tegan Maharaj, Frank Hutter, Atilim Günes Baydin, Sheila McIlraith, Qiqi Gao, Ashwin Acharya, David Krueger, Anca D. Dragan, Philip H. S. Torr, Stuart Russell, Daniel Kahneman, Jan Brauner, Sören Mindermann
Managing AI Risks in an Era of Rapid Progress. CoRR (2023)
Gal Kaplun, Andrey Gurevich, Tal Swisa, Mazor David, Shai Shalev-Shwartz, Eran Malach
SubTuning: Efficient Finetuning for Multi-Task Learning. CoRR (2023)
Gal Kaplun, Eran Malach, Preetum Nakkiran, Shai Shalev-Shwartz
Knowledge Distillation: Bad Models Can Be Good Role Models. CoRR (2022)
Yoav Levine, Itay Dalmedigos, Ori Ram, Yoel Zeldes, Daniel Jannai, Dor Muhlgay, Yoni Osin, Opher Lieber, Barak Lenz, Shai Shalev-Shwartz, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham
Standing on the Shoulders of Giant Frozen Language Models. CoRR (2022)
Alon Brutzkus, Amir Globerson, Eran Malach, Alon Regev Netser, Shai Shalev-Shwartz
Efficient Learning of CNNs using Patch Based Features. ICML (2022)
Ehud Karpas, Omri Abend, Yonatan Belinkov, Barak Lenz, Opher Lieber, Nir Ratner, Yoav Shoham, Hofit Bata, Yoav Levine, Kevin Leyton-Brown, Dor Muhlgay, Noam Rozen, Erez Schwartz, Gal Shachaf, Shai Shalev-Shwartz, Amnon Shashua, Moshe Tennenholtz
MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning. CoRR (2022)
Gal Kaplun, Eran Malach, Preetum Nakkiran, Shai Shalev-Shwartz
Knowledge Distillation: Bad Models Can Be Good Role Models. NeurIPS (2022)
Eran Malach, Shai Shalev-Shwartz
When Hardness of Approximation Meets Hardness of Learning. J. Mach. Learn. Res. 23 (2022)
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks. COLT (2021)
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks. CoRR (2021)
Eran Malach, Shai Shalev-Shwartz
Computational Separation Between Convolutional and Fully-Connected Networks. ICLR (2021)
Eran Malach, Shai Shalev-Shwartz
When Hardness of Approximation Meets Hardness of Learning. CoRR (2020)
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir
Proving the Lottery Ticket Hypothesis: Pruning is All You Need. CoRR (2020)
Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua
On the Ethics of Building AI in a Responsible Manner. CoRR (2020)
Eran Malach, Shai Shalev-Shwartz
The Implications of Local Correlation on Learning Some Deep Functions. NeurIPS (2020)
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir
Proving the Lottery Ticket Hypothesis: Pruning is All You Need. ICML (2020)
Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely
The Implicit Bias of Depth: How Incremental Learning Drives Generalization. ICLR (2020)
Yoav Levine, Barak Lenz, Or Dagan, Ori Ram, Dan Padnos, Or Sharir, Shai Shalev-Shwartz, Amnon Shashua, Yoav Shoham
SenseBERT: Driving Some Sense into BERT. ACL (2020)
Eran Malach, Shai Shalev-Shwartz
Computational Separation Between Convolutional and Fully-Connected Networks. CoRR (2020)
Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz
Distribution Free Learning with Local Queries. ALT (2020)
Eran Malach, Shai Shalev-Shwartz
Is Deeper Better only when Shallow is Good? CoRR (2019)
Eran Malach, Shai Shalev-Shwartz
Is Deeper Better only when Shallow is Good? NeurIPS (2019)
Jonathan Fiat, Eran Malach, Shai Shalev-Shwartz
Decoupling Gating from Linearity. CoRR (2019)
Daniel Gissin, Shai Shalev-Shwartz
Discriminative Active Learning. CoRR (2019)
Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely
The Implicit Bias of Depth: How Incremental Learning Drives Generalization. CoRR (2019)
Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua
Vision Zero: on a Provable Method for Eliminating Roadway Accidents without Compromising Traffic Throughput. CoRR (2019)
Yoav Levine, Barak Lenz, Or Dagan, Dan Padnos, Or Sharir, Shai Shalev-Shwartz, Amnon Shashua, Yoav Shoham
SenseBERT: Driving Some Sense into BERT. CoRR (2019)
Eran Malach, Shai Shalev-Shwartz
Learning Boolean Circuits with Neural Networks. CoRR (2019)
Eran Malach, Shai Shalev-Shwartz
A Provably Correct Algorithm for Deep Learning that Actually Works. CoRR (2018)
Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data. ICLR (Poster) (2018)
Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua
On a Formal Model of Safe and Scalable Self-driving Cars. CoRR (2017)
Alon Gonen, Shai Shalev-Shwartz
Fast Rates for Empirical Risk Minimization of Strict Saddle Problems. CoRR (2017)
Amir Globerson, Roi Livni, Shai Shalev-Shwartz
Effective Semisupervised Learning on Manifolds. COLT (2017)
Alon Gonen, Shai Shalev-Shwartz
Fast Rates for Empirical Risk Minimization of Strict Saddle Problems. COLT (2017)
Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah
Failures of Deep Learning. CoRR (2017)
Alon Gonen, Shai Shalev-Shwartz
Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization. J. Mach. Learn. Res. 18 (2017)
Eran Malach, Shai Shalev-Shwartz
Decoupling "when to update" from "how to update". CoRR (2017)
Elad Hazan, Satyen Kale, Shai Shalev-Shwartz
Near-Optimal Algorithms for Online Matrix Prediction. SIAM J. Comput. 46 (2) (2017)
Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah
Failures of Gradient-Based Deep Learning. ICML (2017)
Eran Malach, Shai Shalev-Shwartz
Decoupling "when to update" from "how to update". NIPS (2017)
Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah
Weight Sharing is Crucial to Succesful Optimization. CoRR (2017)
Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data. CoRR (2017)
Shai Shalev-Shwartz, Yonatan Wexler
Minimizing the Maximal Loss: How and Why? CoRR (2016)
Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving. CoRR (2016)
Shai Shalev-Shwartz, Amnon Shashua
On the Sample Complexity of End-to-end Training vs. Semantic Abstraction Training. CoRR (2016)
Amit Daniely, Shai Shalev-Shwartz
Complexity Theoretic Limitations on Learning DNF's. COLT (2016)
Shai Shalev-Shwartz
SDCA without Duality, Regularization, and Individual Convexity. ICML (2016)
Oren Tadmor, Yonatan Wexler, Tal Rosenwein, Shai Shalev-Shwartz, Amnon Shashua
Learning a Metric Embedding for Face Recognition using the Multibatch Method. CoRR (2016)
Elad Hazan, Kfir Yehuda Levy, Shai Shalev-Shwartz
On Graduated Optimization for Stochastic Non-Convex Problems. ICML (2016)
Shai Shalev-Shwartz, Nir Ben-Zrihem, Aviad Cohen, Amnon Shashua
Long-term Planning by Short-term Prediction. CoRR (2016)
Alon Gonen, Shai Shalev-Shwartz
Tightening the Sample Complexity of Empirical Risk Minimization via Preconditioned Stability. CoRR (2016)
Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz
Distribution Free Learning with Local Queries. CoRR (2016)
Alon Gonen, Shai Shalev-Shwartz
Faster Low-rank Approximation using Adaptive Gap-based Preconditioning. CoRR (2016)
Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz
Solving Ridge Regression using Sketched Preconditioned SVRG. ICML (2016)
Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz
Solving Ridge Regression using Sketched Preconditioned SVRG. CoRR (2016)
Alon Gonen, Dan Rosenbaum, Yonina C. Eldar, Shai Shalev-Shwartz
Subspace Learning with Partial Information. J. Mach. Learn. Res. 17 (2016)
Oren Tadmor, Tal Rosenwein, Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua
Learning a Metric Embedding for Face Recognition using the Multibatch Method. NIPS (2016)
Shai Shalev-Shwartz, Tong Zhang
Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization. Math. Program. 155 (1-2) (2016)
Shai Shalev-Shwartz, Yonatan Wexler
Minimizing the Maximal Loss: How and Why. ICML (2016)
Yossi Arjevani, Shai Shalev-Shwartz, Ohad Shamir
On Lower and Upper Bounds in Smooth and Strongly Convex Optimization. J. Mach. Learn. Res. 17 (2016)
Shai Shalev-Shwartz
SDCA without Duality, Regularization, and Individual Convexity. CoRR (2016)
Elad Hazan, Kfir Y. Levy, Shai Shalev-Shwartz
Beyond Convexity: Stochastic Quasi-Convex Optimization. NIPS (2015)
Shai Shalev-Shwartz
SDCA without Duality. CoRR (2015)
Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel J. Hsu, Tong Zhang
Learning sparse low-threshold linear classifiers. J. Mach. Learn. Res. 16 (2015)
Elad Hazan, Kfir Y. Levy, Shai Shalev-Shwartz
Beyond Convexity: Stochastic Quasi-Convex Optimization. CoRR (2015)
Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz
Multiclass learnability and the ERM principle. J. Mach. Learn. Res. 16 (2015)
Elad Hazan, Kfir Y. Levy, Shai Shalev-Shwartz
On Graduated Optimization for Stochastic Non-Convex Problems. CoRR (2015)
Yossi Arjevani, Shai Shalev-Shwartz, Ohad Shamir
On Lower and Upper Bounds for Smooth and Strongly Convex Optimization Problems. CoRR (2015)
Amit Daniely, Alon Gonen, Shai Shalev-Shwartz
Strongly Adaptive Online Learning. ICML (2015)
Amit Daniely, Alon Gonen, Shai Shalev-Shwartz
Strongly Adaptive Online Learning. CoRR (2015)
Alon Gonen, Shai Shalev-Shwartz
Faster SGD Using Sketched Conditioning. CoRR (2015)
Amit Daniely, Shai Shalev-Shwartz
Complexity theoretic limitations on learning DNF's. CoRR (2014)
Ohad Shamir, Shai Shalev-Shwartz
Matrix completion with the trace norm: learning, bounding, and transducing. J. Mach. Learn. Res. 15 (1) (2014)
Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
On the Computational Efficiency of Training Neural Networks. NIPS (2014)
Amit Daniely, Nati Linial, Shai Shalev-Shwartz
From average case complexity to improper learning complexity. STOC (2014)
Amit Daniely, Nati Linial, Shai Shalev-Shwartz
The Complexity of Learning Halfspaces using Generalized Linear Methods. COLT (2014)
Alon Vinnikov, Shai Shalev-Shwartz
K-means recovers ICA filters when independent components are sparse. ICML (2014)
Shai Shalev-Shwartz, Tong Zhang
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization. ICML (2014)
Amit Daniely, Shai Shalev-Shwartz
Optimal Learners for Multiclass Problems. CoRR (2014)
Alon Gonen, Dan Rosenbaum, Yonina C. Eldar, Shai Shalev-Shwartz
The Sample Complexity of Subspace Learning with Partial Information. CoRR (2014)
Amit Daniely, Shai Shalev-Shwartz
Optimal learners for multiclass problems. COLT (2014)
Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
On the Computational Efficiency of Training Neural Networks. CoRR (2014)
Shai Shalev-Shwartz
SelfieBoost: A Boosting Algorithm for Deep Learning. CoRR (2014)
Shai Shalev-Shwartz, Tong Zhang
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent. NIPS (2013)
Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz
Efficient Active Learning of Halfspaces: an Aggressive Approach. ICML (1) (2013)
Shai Shalev-Shwartz, Tong Zhang
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization. CoRR (2013)
Amit Daniely, Nati Linial, Shai Shalev-Shwartz
More data speeds up training time in learning halfspaces over sparse vectors. NIPS (2013)
Shai Shalev-Shwartz, Tong Zhang
Stochastic dual coordinate ascent methods for regularized loss. J. Mach. Learn. Res. 14 (1) (2013)
Amit Daniely, Nati Linial, Shai Shalev-Shwartz
From average case complexity to improper learning complexity. CoRR (2013)
Shai Shalev-Shwartz, Tong Zhang
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent CoRR (2013)
Roi Livni, David Lehavi, Sagi Schein, Hila Nachlieli, Shai Shalev-Shwartz, Amir Globerson
Vanishing Component Analysis. ICML (1) (2013)
Amit Daniely, Nati Linial, Shai Shalev-Shwartz
More data speeds up training time in learning halfspaces over sparse vectors. CoRR (2013)
Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
A Provably Efficient Algorithm for Training Deep Networks CoRR (2013)
Andrew Cotter, Shai Shalev-Shwartz, Nati Srebro
Learning Optimally Sparse Support Vector Machines. ICML (1) (2013)
Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz
Multiclass learnability and the ERM principle. CoRR (2013)
Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz
Efficient active learning of halfspaces: an aggressive approach. J. Mach. Learn. Res. 14 (1) (2013)
Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz
Efficient Pool-Based Active Learning of Halfspaces CoRR (2012)
Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro
The Kernelized Stochastic Batch Perceptron. ICML (2012)

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Publication Activity (10 Years)

Top Topics

Top Venues

Shai Shalev-Shwartz