Amit Daniely

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 60

Top Topics

Complexity Theoretic

Top Venues

Publications

Amit Daniely, Mariano Schain, Gilad Yehudai
RedEx: Beyond Fixed Representation Methods via Convex Optimization. ALT (2024)
Amit Daniely, Mariano Schain, Gilad Yehudai
RedEx: Beyond Fixed Representation Methods via Convex Optimization. CoRR (2024)
Amit Daniely, Elad Granot
On the Sample Complexity of Two-Layer Networks: Lipschitz Vs. Element-Wise Lipschitz Activation. ALT (2024)
Amit Daniely, Elad Granot
An Exact Poly-Time Membership-Queries Algorithm for Extracting a Three-Layer ReLU Network. ICLR (2023)
Nataly Brukhim, Amit Daniely, Yishay Mansour, Shay Moran
Multiclass Boosting: Simple and Intuitive Weak Learning Criteria. NeurIPS (2023)
Amit Daniely, Nathan Srebro, Gal Vardi
Efficiently Learning Neural Networks: What Assumptions May Suffice? CoRR (2023)
Amit Daniely, Nati Srebro, Gal Vardi
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy. NeurIPS (2023)
Gilad Yehudai, Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain
Locally Optimal Descent for Dynamic Stepsize Scheduling. CoRR (2023)
Amit Daniely, Nati Srebro, Gal Vardi
Most Neural Networks Are Almost Learnable. NeurIPS (2023)
Amit Daniely, Nathan Srebro, Gal Vardi
Most Neural Networks Are Almost Learnable. CoRR (2023)
Nataly Brukhim, Amit Daniely, Yishay Mansour, Shay Moran
Multiclass Boosting: Simple and Intuitive Weak Learning Criteria. CoRR (2023)
Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer
Monotone Learning. COLT (2022)
Amit Daniely, Gal Katzhendler
Approximate Description Length, Covering Numbers, and VC Dimension. CoRR (2022)
Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer
Monotone Learning. CoRR (2022)
Amit Daniely, Elad Granot
On the Sample Complexity of Two-Layer Networks: Lipschitz vs. Element-Wise Lipschitz Activation. CoRR (2022)
Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain
Asynchronous Stochastic Optimization Robust to Arbitrary Delays. NeurIPS (2021)
Amit Daniely, Gal Vardi
From Local Pseudorandom Generators to Hardness of Learning. COLT (2021)
Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain
Asynchronous Stochastic Optimization Robust to Arbitrary Delays. CoRR (2021)
Amit Daniely, Gal Vardi
From Local Pseudorandom Generators to Hardness of Learning. CoRR (2021)
Amit Daniely, Elad Granot
An Exact Poly-Time Membership-Queries Algorithm for Extraction a three-Layer ReLU Network. CoRR (2021)
Amit Daniely, Gal Vardi
Hardness of Learning Neural Networks with Natural Weights. CoRR (2020)
Amit Daniely, Eran Malach
Learning Parities with Neural Networks. NeurIPS (2020)
Amit Daniely, Hadas Shacham
Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations. NeurIPS (2020)
Amit Daniely, Eran Malach
Learning Parities with Neural Networks. CoRR (2020)
Amit Daniely
Memorizing Gaussians with no over-parameterizaion via gradient decent on neural networks. CoRR (2020)
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 (2020)
Amit Daniely, Hadas Schacham
Adversarial Perturbations. CoRR (2020)
Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely
The Implicit Bias of Depth: How Incremental Learning Drives Generalization. ICLR (2020)
Amit Daniely, Gal Vardi
Hardness of Learning Neural Networks with Natural Weights. NeurIPS (2020)
Alon Brutzkus, Amit Daniely, Eran Malach
ID3 Learns Juntas for Smoothed Product Distributions. COLT (2020)
Amit Daniely
Neural Networks Learning and Memorization with (almost) no Over-Parameterization. NeurIPS (2020)
Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz
Distribution Free Learning with Local Queries. ALT (2020)
Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely
The Implicit Bias of Depth: How Incremental Learning Drives Generalization. CoRR (2019)
Amit Daniely
Neural Networks Learning and Memorization with (almost) no Over-Parameterization. CoRR (2019)
Amit Daniely, Elad Granot
Generalization Bounds for Neural Networks via Approximate Description Length. CoRR (2019)
Alon Brutzkus, Amit Daniely, Eran Malach
ID3 Learns Juntas for Smoothed Product Distributions. CoRR (2019)
Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan
Learning Rules-First Classifiers. AISTATS (2019)
Amit Daniely, Yishay Mansour
Competitive ratio versus regret minimization: achieving the best of both worlds. CoRR (2019)
Amit Daniely, Vitaly Feldman
Locally Private Learning without Interaction Requires Separation. NeurIPS (2019)
Amit Daniely, Yishay Mansour
Competitive ratio vs regret minimization: achieving the best of both worlds. ALT (2019)
Alon Brutzkus, Amit Daniely, Eran Malach
On the Optimality of Trees Generated by ID3. CoRR (2019)
Amit Daniely, Vitaly Feldman
Open Problem: Is Margin Sufficient for Non-Interactive Private Distributed Learning? COLT (2019)
Amit Daniely, Elad Granot
Generalization Bounds for Neural Networks via Approximate Description Length. NeurIPS (2019)
Craig Boutilier, Alon Cohen, Amit Daniely, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans
Planning and Learning with Stochastic Action Sets. CoRR (2018)
Amit Daniely, Vitaly Feldman
Learning without Interaction Requires Separation. CoRR (2018)
Amit Daniely, Michael Schapira, Gal Shahaf
Inapproximability of Truthful Mechanisms via Generalizations of the Vapnik-Chervonenkis Dimension. SIAM J. Comput. 47 (1) (2018)
Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan
Learning with Rules. CoRR (2018)
Amit Daniely
Depth Separation for Neural Networks. COLT (2017)
Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar
Short and Deep: Sketching and Neural Networks. ICLR (Workshop) (2017)
Amit Daniely, Roy Frostig, Vineet Gupta, Yoram Singer
Random Features for Compositional Kernels. CoRR (2017)
Amit Daniely
SGD Learns the Conjugate Kernel Class of the Network. CoRR (2017)
Amit Daniely
SGD Learns the Conjugate Kernel Class of the Network. NIPS (2017)
Amit Daniely
Depth Separation for Neural Networks. CoRR (2017)
Amit Daniely, Roy Frostig, Yoram Singer
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity. NIPS (2016)
Amit Daniely, Shai Shalev-Shwartz
Complexity Theoretic Limitations on Learning DNF's. COLT (2016)
Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz
Distribution Free Learning with Local Queries. CoRR (2016)
Gali Noti, Effi Levi, Yoav Kolumbus, Amit Daniely
Behavior-Based Machine-Learning: A Hybrid Approach for Predicting Human Decision Making. CoRR (2016)
Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar
Sketching and Neural Networks. CoRR (2016)
Amit Daniely
Complexity theoretic limitations on learning halfspaces. STOC (2016)
Amit Daniely, Roy Frostig, Yoram Singer
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity. CoRR (2016)
Amit Daniely
Complexity Theoretic Limitations on Learning Halfspaces. CoRR (2015)
Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz
Multiclass learnability and the ERM principle. J. Mach. Learn. Res. 16 (2015)
Amit Daniely, Alon Gonen, Shai Shalev-Shwartz
Strongly Adaptive Online Learning. ICML (2015)
Amit Daniely, Michael Schapira, Gal Shahaf
Inapproximability of Truthful Mechanisms via Generalizations of the VC Dimension. STOC (2015)
Amit Daniely, Alon Gonen, Shai Shalev-Shwartz
Strongly Adaptive Online Learning. CoRR (2015)
Amit Daniely
A PTAS for Agnostically Learning Halfspaces. COLT (2015)
Amit Daniely, Shai Shalev-Shwartz
Complexity theoretic limitations on learning DNF's. CoRR (2014)
Maria-Florina Balcan, Amit Daniely, Ruta Mehta, Ruth Urner, Vijay V. Vazirani
Learning Economic Parameters from Revealed Preferences. WINE (2014)
Maria-Florina Balcan, Amit Daniely, Ruta Mehta, Ruth Urner, Vijay V. Vazirani
Learning Economic Parameters from Revealed Preferences. CoRR (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)
Amit Daniely
A PTAS for Agnostically Learning Halfspaces. CoRR (2014)
Amit Daniely, Shai Shalev-Shwartz
Optimal Learners for Multiclass Problems. CoRR (2014)
Amit Daniely, Michael Schapira, Gal Shahaf
Inapproximability of Truthful Mechanisms via Generalizations of the VC Dimension. CoRR (2014)
Amit Daniely, Shai Shalev-Shwartz
Optimal learners for multiclass problems. COLT (2014)
Amit Daniely, Tom Helbertal
The price of bandit information in multiclass online classification CoRR (2013)
Amit Daniely, Nati Linial, Shai Shalev-Shwartz
More data speeds up training time in learning halfspaces over sparse vectors. NIPS (2013)
Amit Daniely, Nati Linial, Shai Shalev-Shwartz
From average case complexity to improper learning complexity. CoRR (2013)
Amit Daniely, Nati Linial, Shai Shalev-Shwartz
More data speeds up training time in learning halfspaces over sparse vectors. CoRR (2013)
Amit Daniely, Tom Helbertal
The price of bandit information in multiclass online classification. COLT (2013)
Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz
Multiclass learnability and the ERM principle. CoRR (2013)
Yonatan Bilu, Amit Daniely, Nati Linial, Michael E. Saks
On the practically interesting instances of MAXCUT. STACS (2013)
Amit Daniely, Sivan Sabato, Shai Shalev-Shwartz
Multiclass Learning Approaches: A Theoretical Comparison with Implications. NIPS (2012)
Amit Daniely, Nati Linial, Michael E. Saks
Clustering is difficult only when it does not matter CoRR (2012)
Amit Daniely, Nathan Linial
Tight products and graph expansion. J. Graph Theory 69 (4) (2012)
Yonatan Bilu, Amit Daniely, Nati Linial, Michael E. Saks
On the practically interesting instances of MAXCUT CoRR (2012)
Amit Daniely, Nati Linial, Shai Shalev-Shwartz
The error rate of learning halfspaces using Kernel-SVMs CoRR (2012)
Amit Daniely, Sivan Sabato, Shai Shalev-Shwartz
Multiclass Learning Approaches: A Theoretical Comparison with Implications CoRR (2012)
Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz
Multiclass Learnability and the ERM principle. COLT (2011)
Amit Daniely, Nathan Linial
Tight products and Expansion CoRR (2010)