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Amit Daniely

Affiliations:

The Hebrew University of Jerusalem, Israel

According to our database¹, Amit Daniely authored at least 59 papers between 2010 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of two.

Timeline

Legend:

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In proceedings

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PhD thesis

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Online presence:

On csauthors.net:

Bibliography

2024

RedEx: Beyond Fixed Representation Methods via Convex Optimization.

[BibT_eX]

[DOI]

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CoRR, 2024

2023

Locally Optimal Descent for Dynamic Stepsize Scheduling.

[BibT_eX]

[DOI]

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CoRR, 2023

Efficiently Learning Neural Networks: What Assumptions May Suffice?

[BibT_eX]

[DOI]

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CoRR, 2023

Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy.

[BibT_eX]

[DOI]

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Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Most Neural Networks Are Almost Learnable.

[BibT_eX]

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Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Multiclass Boosting: Simple and Intuitive Weak Learning Criteria.

[BibT_eX]

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Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

An Exact Poly-Time Membership-Queries Algorithm for Extracting a Three-Layer ReLU Network.

[BibT_eX]

[DOI]

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Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

On the Sample Complexity of Two-Layer Networks: Lipschitz vs. Element-Wise Lipschitz Activation.

[BibT_eX]

[DOI]

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CoRR, 2022

Approximate Description Length, Covering Numbers, and VC Dimension.

[BibT_eX]

[DOI]

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Gal Katzhendler

CoRR, 2022

Monotone Learning.

[BibT_eX]

[DOI]

Olivier Bousquet

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Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

An Exact Poly-Time Membership-Queries Algorithm for Extraction a three-Layer ReLU Network.

[BibT_eX]

[DOI]

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CoRR, 2021

Asynchronous Stochastic Optimization Robust to Arbitrary Delays.

[BibT_eX]

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Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

From Local Pseudorandom Generators to Hardness of Learning.

[BibT_eX]

[DOI]

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Proceedings of the Conference on Learning Theory, 2021

2020

Most ReLU Networks Suffer from 𝓁<sup>2</sup> Adversarial Perturbations.

[BibT_eX]

[DOI]

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CoRR, 2020

Memorizing Gaussians with no over-parameterizaion via gradient decent on neural networks.

[BibT_eX]

[DOI]

CoRR, 2020

On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions.

[BibT_eX]

[DOI]

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Stefanie Jegelka

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CoRR, 2020

Hardness of Learning Neural Networks with Natural Weights.

[BibT_eX]

[DOI]

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Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations.

[BibT_eX]

[DOI]

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Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Parities with Neural Networks.

[BibT_eX]

[DOI]

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Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Neural Networks Learning and Memorization with (almost) no Over-Parameterization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

The Implicit Bias of Depth: How Incremental Learning Drives Generalization.

[BibT_eX]

[DOI]

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Shai Shalev-Shwartz

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Proceedings of the 8th International Conference on Learning Representations, 2020

ID3 Learns Juntas for Smoothed Product Distributions.

[BibT_eX]

[DOI]

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Proceedings of the Conference on Learning Theory, 2020

Distribution Free Learning with Local Queries.

[BibT_eX]

[DOI]

Galit Bary-Weisberg

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Shai Shalev-Shwartz

Proceedings of the Algorithmic Learning Theory, 2020

2019

On the Optimality of Trees Generated by ID3.

[BibT_eX]

[DOI]

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CoRR, 2019

Competitive ratio versus regret minimization: achieving the best of both worlds.

[BibT_eX]

[DOI]

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CoRR, 2019

Generalization Bounds for Neural Networks via Approximate Description Length.

[BibT_eX]

[DOI]

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Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Locally Private Learning without Interaction Requires Separation.

[BibT_eX]

[DOI]

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Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Open Problem: Is Margin Sufficient for Non-Interactive Private Distributed Learning?

[BibT_eX]

[DOI]

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Proceedings of the Conference on Learning Theory, 2019

Competitive ratio vs regret minimization: achieving the best of both worlds.

[BibT_eX]

[DOI]

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Proceedings of the Algorithmic Learning Theory, 2019

Learning Rules-First Classifiers.

[BibT_eX]

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Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Inapproximability of Truthful Mechanisms via Generalizations of the Vapnik-Chervonenkis Dimension.

[BibT_eX]

[DOI]

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Michael Schapira

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SIAM J. Comput., 2018

Learning without Interaction Requires Separation.

[BibT_eX]

[DOI]

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CoRR, 2018

Planning and Learning with Stochastic Action Sets.

[BibT_eX]

[DOI]

Craig Boutilier

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Avinatan Hassidim

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Martin Mladenov

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Dale Schuurmans

CoRR, 2018

Learning with Rules.

[BibT_eX]

[DOI]

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CoRR, 2018

2017

Random Features for Compositional Kernels.

[BibT_eX]

[DOI]

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CoRR, 2017

SGD Learns the Conjugate Kernel Class of the Network.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Short and Deep: Sketching and Neural Networks.

[BibT_eX]

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Proceedings of the 5th International Conference on Learning Representations, 2017

Depth Separation for Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 30th Conference on Learning Theory, 2017

2016

Behavior-Based Machine-Learning: A Hybrid Approach for Predicting Human Decision Making.

[BibT_eX]

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CoRR, 2016

Sketching and Neural Networks.

[BibT_eX]

[DOI]

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CoRR, 2016

Complexity theoretic limitations on learning halfspaces.

[BibT_eX]

[DOI]

Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, 2016

Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity.

[BibT_eX]

[DOI]

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Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Complexity Theoretic Limitations on Learning DNF's.

[BibT_eX]

[DOI]

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Shai Shalev-Shwartz

Proceedings of the 29th Conference on Learning Theory, 2016

2015

Multiclass learnability and the ERM principle.

[BibT_eX]

[DOI]

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Shai Shalev-Shwartz

J. Mach. Learn. Res., 2015

Inapproximability of Truthful Mechanisms via Generalizations of the VC Dimension.

[BibT_eX]

[DOI]

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Michael Schapira

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Proceedings of the Forty-Seventh Annual ACM on Symposium on Theory of Computing, 2015

Strongly Adaptive Online Learning.

[BibT_eX]

[DOI]

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Shai Shalev-Shwartz

Proceedings of the 32nd International Conference on Machine Learning, 2015

A PTAS for Agnostically Learning Halfspaces.

[BibT_eX]

[DOI]

Proceedings of The 28th Conference on Learning Theory, 2015

2014

Learning Economic Parameters from Revealed Preferences.

[BibT_eX]

[DOI]

Maria-Florina Balcan

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Vijay V. Vazirani

Proceedings of the Web and Internet Economics - 10th International Conference, 2014

From average case complexity to improper learning complexity.

[BibT_eX]

[DOI]

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Shai Shalev-Shwartz

Proceedings of the Symposium on Theory of Computing, 2014

Optimal learners for multiclass problems.

[BibT_eX]

[DOI]

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Shai Shalev-Shwartz

Proceedings of The 27th Conference on Learning Theory, 2014

The Complexity of Learning Halfspaces using Generalized Linear Methods.

[BibT_eX]

[DOI]

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Shai Shalev-Shwartz

Proceedings of The 27th Conference on Learning Theory, 2014

2013

On the practically interesting instances of MAXCUT.

[BibT_eX]

[DOI]

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Michael E. Saks

Proceedings of the 30th International Symposium on Theoretical Aspects of Computer Science, 2013

More data speeds up training time in learning halfspaces over sparse vectors.

[BibT_eX]

[DOI]

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Shai Shalev-Shwartz

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

The price of bandit information in multiclass online classification.

[BibT_eX]

[DOI]

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Proceedings of the COLT 2013, 2013

2012

Tight products and graph expansion.

[BibT_eX]

[DOI]

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J. Graph Theory, 2012

The error rate of learning halfspaces using Kernel-SVMs

[BibT_eX]

[DOI]

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Shai Shalev-Shwartz

CoRR, 2012

Clustering is difficult only when it does not matter

[BibT_eX]

[DOI]

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Michael E. Saks

CoRR, 2012

Multiclass Learning Approaches: A Theoretical Comparison with Implications.

[BibT_eX]

[DOI]

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Shai Shalev-Shwartz

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2010

Tight products and Expansion

[BibT_eX]

[DOI]

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CoRR, 2010

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