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ORCIDAmit Daniely
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- affiliation: The Hebrew University of Jerusalem, Israel
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2020 – today
- 2024
[c39]Amit Daniely, Elad Granot:
On the Sample Complexity of Two-Layer Networks: Lipschitz Vs. Element-Wise Lipschitz Activation. ALT 2024: 505-517- [c38]Amit Daniely, Mariano Schain, Gilad Yehudai:
RedEx: Beyond Fixed Representation Methods via Convex Optimization. ALT 2024: 518-543 - [i48]Amit Daniely, Mariano Schain, Gilad Yehudai:
RedEx: Beyond Fixed Representation Methods via Convex Optimization. CoRR abs/2401.07606 (2024) - 2023
- [c37]Amit Daniely, Elad Granot:
An Exact Poly-Time Membership-Queries Algorithm for Extracting a Three-Layer ReLU Network. ICLR 2023 - [c36]Nataly Brukhim, Amit Daniely, Yishay Mansour, Shay Moran:
Multiclass Boosting: Simple and Intuitive Weak Learning Criteria. NeurIPS 2023 - [c35]Amit Daniely, Nati Srebro, Gal Vardi:
Most Neural Networks Are Almost Learnable. NeurIPS 2023 - [c34]Amit Daniely, Nati Srebro, Gal Vardi:
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy. NeurIPS 2023 - [i47]Amit Daniely, Nathan Srebro, Gal Vardi:
Efficiently Learning Neural Networks: What Assumptions May Suffice? CoRR abs/2302.07426 (2023) - [i46]Amit Daniely, Nathan Srebro, Gal Vardi:
Most Neural Networks Are Almost Learnable. CoRR abs/2305.16508 (2023) - [i45]Nataly Brukhim, Amit Daniely, Yishay Mansour, Shay Moran:
Multiclass Boosting: Simple and Intuitive Weak Learning Criteria. CoRR abs/2307.00642 (2023) - [i44]Gilad Yehudai, Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain:
Locally Optimal Descent for Dynamic Stepsize Scheduling. CoRR abs/2311.13877 (2023) - 2022
- [c33]Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer:
Monotone Learning. COLT 2022: 842-866 - [i43]Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer:
Monotone Learning. CoRR abs/2202.05246 (2022) - [i42]Amit Daniely, Gal Katzhendler:
Approximate Description Length, Covering Numbers, and VC Dimension. CoRR abs/2209.12882 (2022) - [i41]Amit Daniely, Elad Granot:
On the Sample Complexity of Two-Layer Networks: Lipschitz vs. Element-Wise Lipschitz Activation. CoRR abs/2211.09634 (2022) - 2021
- [c32]Amit Daniely, Gal Vardi:
From Local Pseudorandom Generators to Hardness of Learning. COLT 2021: 1358-1394 - [c31]Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain:
Asynchronous Stochastic Optimization Robust to Arbitrary Delays. NeurIPS 2021: 9024-9035 - [i40]Amit Daniely, Gal Vardi:
From Local Pseudorandom Generators to Hardness of Learning. CoRR abs/2101.08303 (2021) - [i39]Amit Daniely, Elad Granot:
An Exact Poly-Time Membership-Queries Algorithm for Extraction a three-Layer ReLU Network. CoRR abs/2105.09673 (2021) - [i38]Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain:
Asynchronous Stochastic Optimization Robust to Arbitrary Delays. CoRR abs/2106.11879 (2021) - 2020
- [c30]Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz:
Distribution Free Learning with Local Queries. ALT 2020: 133-147 - [c29]Alon Brutzkus, Amit Daniely, Eran Malach:
ID3 Learns Juntas for Smoothed Product Distributions. COLT 2020: 902-915 - [c28]Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely:
The Implicit Bias of Depth: How Incremental Learning Drives Generalization. ICLR 2020 - [c27]Amit Daniely:
Neural Networks Learning and Memorization with (almost) no Over-Parameterization. NeurIPS 2020 - [c26]Amit Daniely, Eran Malach:
Learning Parities with Neural Networks. NeurIPS 2020 - [c25]Amit Daniely, Hadas Shacham:
Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations. NeurIPS 2020 - [c24]Amit Daniely, Gal Vardi:
Hardness of Learning Neural Networks with Natural Weights. NeurIPS 2020 - [i37]Yossi Arjevani, Amit Daniely, Stefanie Jegelka, Hongzhou Lin:
On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions. CoRR abs/2002.03273 (2020) - [i36]Amit Daniely, Eran Malach:
Learning Parities with Neural Networks. CoRR abs/2002.07400 (2020) - [i35]Amit Daniely:
Memorizing Gaussians with no over-parameterizaion via gradient decent on neural networks. CoRR abs/2003.12895 (2020) - [i34]Amit Daniely, Gal Vardi:
Hardness of Learning Neural Networks with Natural Weights. CoRR abs/2006.03177 (2020) - [i33]Amit Daniely, Hadas Schacham:
Most ReLU Networks Suffer from 𝓁2 Adversarial Perturbations. CoRR abs/2010.14927 (2020)
2010 – 2019
- 2019
- [c23]Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan:
Learning Rules-First Classifiers. AISTATS 2019: 1398-1406 - [c22]Amit Daniely, Yishay Mansour:
Competitive ratio vs regret minimization: achieving the best of both worlds. ALT 2019: 333-368 - [c21]Amit Daniely, Vitaly Feldman:
Open Problem: Is Margin Sufficient for Non-Interactive Private Distributed Learning? COLT 2019: 3180-3184 - [c20]Amit Daniely, Elad Granot:
Generalization Bounds for Neural Networks via Approximate Description Length. NeurIPS 2019: 12988-12996 - [c19]Amit Daniely, Vitaly Feldman:
Locally Private Learning without Interaction Requires Separation. NeurIPS 2019: 14975-14986 - [i32]Amit Daniely, Yishay Mansour:
Competitive ratio versus regret minimization: achieving the best of both worlds. CoRR abs/1904.03602 (2019) - [i31]Alon Brutzkus, Amit Daniely, Eran Malach:
ID3 Learns Juntas for Smoothed Product Distributions. CoRR abs/1906.08654 (2019) - [i30]Alon Brutzkus, Amit Daniely, Eran Malach:
On the Optimality of Trees Generated by ID3. CoRR abs/1907.05444 (2019) - [i29]Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely:
The Implicit Bias of Depth: How Incremental Learning Drives Generalization. CoRR abs/1909.12051 (2019) - [i28]Amit Daniely, Elad Granot:
Generalization Bounds for Neural Networks via Approximate Description Length. CoRR abs/1910.05697 (2019) - [i27]Amit Daniely:
Neural Networks Learning and Memorization with (almost) no Over-Parameterization. CoRR abs/1911.09873 (2019) - 2018
- [j3]Amit Daniely, Michael Schapira, Gal Shahaf:
Inapproximability of Truthful Mechanisms via Generalizations of the Vapnik-Chervonenkis Dimension. SIAM J. Comput. 47(1): 96-120 (2018) - [i26]Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan:
Learning with Rules. CoRR abs/1803.03155 (2018) - [i25]Craig Boutilier, Alon Cohen, Amit Daniely, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans:
Planning and Learning with Stochastic Action Sets. CoRR abs/1805.02363 (2018) - [i24]Amit Daniely, Vitaly Feldman:
Learning without Interaction Requires Separation. CoRR abs/1809.09165 (2018) - 2017
- [c18]Amit Daniely:
Depth Separation for Neural Networks. COLT 2017: 690-696 - [c17]Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar:
Short and Deep: Sketching and Neural Networks. ICLR (Workshop) 2017 - [c16]Amit Daniely:
SGD Learns the Conjugate Kernel Class of the Network. NIPS 2017: 2422-2430 - [i23]Amit Daniely:
Depth Separation for Neural Networks. CoRR abs/1702.08489 (2017) - [i22]Amit Daniely:
SGD Learns the Conjugate Kernel Class of the Network. CoRR abs/1702.08503 (2017) - [i21]Amit Daniely, Roy Frostig, Vineet Gupta, Yoram Singer:
Random Features for Compositional Kernels. CoRR abs/1703.07872 (2017) - 2016
- [c15]Amit Daniely, Shai Shalev-Shwartz:
Complexity Theoretic Limitations on Learning DNF's. COLT 2016: 815-830 - [c14]Amit Daniely, Roy Frostig, Yoram Singer:
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity. NIPS 2016: 2253-2261 - [c13]Amit Daniely:
Complexity theoretic limitations on learning halfspaces. STOC 2016: 105-117 - [i20]Amit Daniely, Roy Frostig, Yoram Singer:
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity. CoRR abs/1602.05897 (2016) - [i19]Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz:
Distribution Free Learning with Local Queries. CoRR abs/1603.03714 (2016) - [i18]Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar:
Sketching and Neural Networks. CoRR abs/1604.05753 (2016) - [i17]Gali Noti, Effi Levi, Yoav Kolumbus, Amit Daniely:
Behavior-Based Machine-Learning: A Hybrid Approach for Predicting Human Decision Making. CoRR abs/1611.10228 (2016) - 2015
- [j2]Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz:
Multiclass learnability and the ERM principle. J. Mach. Learn. Res. 16: 2377-2404 (2015) - [c12]Amit Daniely:
A PTAS for Agnostically Learning Halfspaces. COLT 2015: 484-502 - [c11]Amit Daniely, Alon Gonen, Shai Shalev-Shwartz:
Strongly Adaptive Online Learning. ICML 2015: 1405-1411 - [c10]Amit Daniely, Michael Schapira, Gal Shahaf:
Inapproximability of Truthful Mechanisms via Generalizations of the VC Dimension. STOC 2015: 401-408 - [i16]Amit Daniely, Alon Gonen, Shai Shalev-Shwartz:
Strongly Adaptive Online Learning. CoRR abs/1502.07073 (2015) - [i15]Amit Daniely:
Complexity Theoretic Limitations on Learning Halfspaces. CoRR abs/1505.05800 (2015) - 2014
- [c9]Amit Daniely, Nati Linial, Shai Shalev-Shwartz:
The Complexity of Learning Halfspaces using Generalized Linear Methods. COLT 2014: 244-286 - [c8]Amit Daniely, Shai Shalev-Shwartz:
Optimal learners for multiclass problems. COLT 2014: 287-316 - [c7]Amit Daniely, Nati Linial, Shai Shalev-Shwartz:
From average case complexity to improper learning complexity. STOC 2014: 441-448 - [c6]Maria-Florina Balcan, Amit Daniely, Ruta Mehta, Ruth Urner, Vijay V. Vazirani:
Learning Economic Parameters from Revealed Preferences. WINE 2014: 338-353 - [i14]Amit Daniely, Shai Shalev-Shwartz:
Complexity theoretic limitations on learning DNF's. CoRR abs/1404.3378 (2014) - [i13]Amit Daniely, Shai Shalev-Shwartz:
Optimal Learners for Multiclass Problems. CoRR abs/1405.2420 (2014) - [i12]Maria-Florina Balcan, Amit Daniely, Ruta Mehta, Ruth Urner, Vijay V. Vazirani:
Learning Economic Parameters from Revealed Preferences. CoRR abs/1407.7937 (2014) - [i11]Amit Daniely:
A PTAS for Agnostically Learning Halfspaces. CoRR abs/1410.7050 (2014) - [i10]Amit Daniely, Michael Schapira, Gal Shahaf:
Inapproximability of Truthful Mechanisms via Generalizations of the VC Dimension. CoRR abs/1412.6265 (2014) - 2013
- [c5]Amit Daniely, Tom Helbertal:
The price of bandit information in multiclass online classification. COLT 2013: 93-104 - [c4]Amit Daniely, Nati Linial, Shai Shalev-Shwartz:
More data speeds up training time in learning halfspaces over sparse vectors. NIPS 2013: 145-153 - [c3]Yonatan Bilu, Amit Daniely, Nati Linial, Michael E. Saks:
On the practically interesting instances of MAXCUT. STACS 2013: 526-537 - [i9]Amit Daniely, Tom Helbertal:
The price of bandit information in multiclass online classification. CoRR abs/1302.1043 (2013) - [i8]Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz:
Multiclass learnability and the ERM principle. CoRR abs/1308.2893 (2013) - [i7]Amit Daniely, Nati Linial, Shai Shalev-Shwartz:
More data speeds up training time in learning halfspaces over sparse vectors. CoRR abs/1311.2271 (2013) - [i6]Amit Daniely, Nati Linial, Shai Shalev-Shwartz:
From average case complexity to improper learning complexity. CoRR abs/1311.2272 (2013) - 2012
- [j1]Amit Daniely, Nathan Linial:
Tight products and graph expansion. J. Graph Theory 69(4): 426-440 (2012) - [c2]Amit Daniely, Sivan Sabato, Shai Shalev-Shwartz:
Multiclass Learning Approaches: A Theoretical Comparison with Implications. NIPS 2012: 494-502 - [i5]Amit Daniely, Nati Linial, Michael E. Saks:
Clustering is difficult only when it does not matter. CoRR abs/1205.4891 (2012) - [i4]Yonatan Bilu, Amit Daniely, Nati Linial, Michael E. Saks:
On the practically interesting instances of MAXCUT. CoRR abs/1205.4893 (2012) - [i3]Amit Daniely, Sivan Sabato, Shai Shalev-Shwartz:
Multiclass Learning Approaches: A Theoretical Comparison with Implications. CoRR abs/1205.6432 (2012) - [i2]Amit Daniely, Nati Linial, Shai Shalev-Shwartz:
The error rate of learning halfspaces using Kernel-SVMs. CoRR abs/1211.0616 (2012) - 2011
- [c1]Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz:
Multiclass Learnability and the ERM principle. COLT 2011: 207-232 - 2010
- [i1]Amit Daniely, Nathan Linial:
Tight products and Expansion. CoRR abs/1001.3661 (2010)
Coauthor Index
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