Amit Daniely - RankMe

96 papers A* 28A 3B 4Journal 55Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Deep Networks Learn Deep Hierarchical Models. CoRR Amit Daniely
2026	J	`jnl`	Most Convolutional Networks Suffer from Small Adversarial Perturbations. CoRR Amit Daniely, Idan Mehalel
2025	A*	`conf`	Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{SO}(d)$. COLT Amit Daniely
2025	J	`jnl`	Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on so(d). CoRR Amit Daniely
2025	A	`conf`	Locally Optimal Descent for Dynamic Stepsize Scheduling. AISTATS Gilad Yehudai, Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain
2025	J	`jnl`	Online Learning of Neural Networks. CoRR Amit Daniely, Idan Mehalel, Elchanan Mossel
2024	B	`conf`	On the Sample Complexity of Two-Layer Networks: Lipschitz Vs. Element-Wise Lipschitz Activation. ALT Amit Daniely, Elad Granot
2024	B	`conf`	RedEx: Beyond Fixed Representation Methods via Convex Optimization. ALT Amit Daniely, Mariano Schain, Gilad Yehudai
2024	J	`jnl`	RedEx: Beyond Fixed Representation Methods via Convex Optimization. CoRR Amit Daniely, Mariano Schain, Gilad Yehudai
2023	A*	`conf`	An Exact Poly-Time Membership-Queries Algorithm for Extracting a Three-Layer ReLU Network. ICLR Amit Daniely, Elad Granot
2023	A*	`conf`	Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy. NeurIPS Amit Daniely, Nati Srebro, Gal Vardi
2023	J	`jnl`	Efficiently Learning Neural Networks: What Assumptions May Suffice? CoRR Amit Daniely, Nathan Srebro, Gal Vardi
2023	J	`jnl`	Locally Optimal Descent for Dynamic Stepsize Scheduling. CoRR Gilad Yehudai, Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain
2023	A*	`conf`	Most Neural Networks Are Almost Learnable. NeurIPS Amit Daniely, Nati Srebro, Gal Vardi
2023	J	`jnl`	Most Neural Networks Are Almost Learnable. CoRR Amit Daniely, Nathan Srebro, Gal Vardi
2023	A*	`conf`	Multiclass Boosting: Simple and Intuitive Weak Learning Criteria. NeurIPS Nataly Brukhim, Amit Daniely, Yishay Mansour, Shay Moran
2023	J	`jnl`	Multiclass Boosting: Simple and Intuitive Weak Learning Criteria. CoRR Nataly Brukhim, Amit Daniely, Yishay Mansour, Shay Moran
2022	J	`jnl`	Approximate Description Length, Covering Numbers, and VC Dimension. CoRR Amit Daniely, Gal Katzhendler
2022	A*	`conf`	Monotone Learning. COLT Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer
2022	J	`jnl`	Monotone Learning. CoRR Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer
2022	J	`jnl`	On the Sample Complexity of Two-Layer Networks: Lipschitz vs. Element-Wise Lipschitz Activation. CoRR Amit Daniely, Elad Granot
2021	J	`jnl`	An Exact Poly-Time Membership-Queries Algorithm for Extraction a three-Layer ReLU Network. CoRR Amit Daniely, Elad Granot
2021	A*	`conf`	Asynchronous Stochastic Optimization Robust to Arbitrary Delays. NeurIPS Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain
2021	J	`jnl`	Asynchronous Stochastic Optimization Robust to Arbitrary Delays. CoRR Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain
2021	A*	`conf`	From Local Pseudorandom Generators to Hardness of Learning. COLT Amit Daniely, Gal Vardi
2021	J	`jnl`	From Local Pseudorandom Generators to Hardness of Learning. CoRR Amit Daniely, Gal Vardi
2020	B	`conf`	Distribution Free Learning with Local Queries. ALT Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz
2020	A*	`conf`	Hardness of Learning Neural Networks with Natural Weights. NeurIPS Amit Daniely, Gal Vardi
2020	J	`jnl`	Hardness of Learning Neural Networks with Natural Weights. CoRR Amit Daniely, Gal Vardi
2020	A*	`conf`	ID3 Learns Juntas for Smoothed Product Distributions. COLT Alon Brutzkus, Amit Daniely, Eran Malach
2020	A*	`conf`	Learning Parities with Neural Networks. NeurIPS Amit Daniely, Eran Malach
2020	J	`jnl`	Learning Parities with Neural Networks. CoRR Amit Daniely, Eran Malach
2020	J	`jnl`	Memorizing Gaussians with no over-parameterizaion via gradient decent on neural networks. CoRR Amit Daniely
2020	A*	`conf`	Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations. NeurIPS Amit Daniely, Hadas Shacham
2020	J	`jnl`	Most ReLU Networks Suffer from 𝓁 CoRR Amit Daniely, Hadas Schacham
2020	A*	`conf`	Neural Networks Learning and Memorization with (almost) no Over-Parameterization. NeurIPS Amit Daniely
2020	J	`jnl`	On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions. CoRR Yossi Arjevani, Amit Daniely, Stefanie Jegelka, Hongzhou Lin
2020	A*	`conf`	The Implicit Bias of Depth: How Incremental Learning Drives Generalization. ICLR Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely
2019	J	`jnl`	Competitive ratio versus regret minimization: achieving the best of both worlds. CoRR Amit Daniely, Yishay Mansour
2019	B	`conf`	Competitive ratio vs regret minimization: achieving the best of both worlds. ALT Amit Daniely, Yishay Mansour
2019	A*	`conf`	Generalization Bounds for Neural Networks via Approximate Description Length. NeurIPS Amit Daniely, Elad Granot
2019	J	`jnl`	Generalization Bounds for Neural Networks via Approximate Description Length. CoRR Amit Daniely, Elad Granot
2019	J	`jnl`	ID3 Learns Juntas for Smoothed Product Distributions. CoRR Alon Brutzkus, Amit Daniely, Eran Malach
2019	A	`conf`	Learning Rules-First Classifiers. AISTATS Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan
2019	A*	`conf`	Locally Private Learning without Interaction Requires Separation. NeurIPS Amit Daniely, Vitaly Feldman
2019	J	`jnl`	Neural Networks Learning and Memorization with (almost) no Over-Parameterization. CoRR Amit Daniely
2019	J	`jnl`	On the Optimality of Trees Generated by ID3. CoRR Alon Brutzkus, Amit Daniely, Eran Malach
2019	A*	`conf`	Open Problem: Is Margin Sufficient for Non-Interactive Private Distributed Learning? COLT Amit Daniely, Vitaly Feldman
2019	J	`jnl`	The Implicit Bias of Depth: How Incremental Learning Drives Generalization. CoRR Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely
2018	J	`jnl`	Inapproximability of Truthful Mechanisms via Generalizations of the Vapnik-Chervonenkis Dimension. SIAM J. Comput. Amit Daniely, Michael Schapira, Gal Shahaf
2018	J	`jnl`	Learning with Rules. CoRR Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan
2018	J	`jnl`	Learning without Interaction Requires Separation. CoRR Amit Daniely, Vitaly Feldman
2018	J	`jnl`	Planning and Learning with Stochastic Action Sets. CoRR Craig Boutilier, Alon Cohen, Amit Daniely, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans
2017	A*	`conf`	Depth Separation for Neural Networks. COLT Amit Daniely
2017	J	`jnl`	Depth Separation for Neural Networks. CoRR Amit Daniely
2017	J	`jnl`	Random Features for Compositional Kernels. CoRR Amit Daniely, Roy Frostig, Vineet Gupta, Yoram Singer
2017	—	`conf`	SGD Learns the Conjugate Kernel Class of the Network. NIPS Amit Daniely
2017	J	`jnl`	SGD Learns the Conjugate Kernel Class of the Network. CoRR Amit Daniely
2017	—	`conf`	Short and Deep: Sketching and Neural Networks. ICLR (Workshop) Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar
2016	J	`jnl`	Behavior-Based Machine-Learning: A Hybrid Approach for Predicting Human Decision Making. CoRR Gali Noti, Effi Levi, Yoav Kolumbus, Amit Daniely
2016	A*	`conf`	Complexity Theoretic Limitations on Learning DNF's. COLT Amit Daniely, Shai Shalev-Shwartz
2016	A*	`conf`	Complexity theoretic limitations on learning halfspaces. STOC Amit Daniely
2016	J	`jnl`	Distribution Free Learning with Local Queries. CoRR Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz
2016	J	`jnl`	Sketching and Neural Networks. CoRR Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar
2016	—	`conf`	Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity. NIPS Amit Daniely, Roy Frostig, Yoram Singer
2016	J	`jnl`	Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity. CoRR Amit Daniely, Roy Frostig, Yoram Singer
2015	A*	`conf`	A PTAS for Agnostically Learning Halfspaces. COLT Amit Daniely
2015	J	`jnl`	Complexity Theoretic Limitations on Learning Halfspaces. CoRR Amit Daniely
2015	A*	`conf`	Inapproximability of Truthful Mechanisms via Generalizations of the VC Dimension. STOC Amit Daniely, Michael Schapira, Gal Shahaf
2015	J	`jnl`	Multiclass learnability and the ERM principle. J. Mach. Learn. Res. Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz
2015	A*	`conf`	Strongly Adaptive Online Learning. ICML Amit Daniely, Alon Gonen, Shai Shalev-Shwartz
2015	J	`jnl`	Strongly Adaptive Online Learning. CoRR Amit Daniely, Alon Gonen, Shai Shalev-Shwartz
2014	J	`jnl`	A PTAS for Agnostically Learning Halfspaces. CoRR Amit Daniely
2014	J	`jnl`	Complexity theoretic limitations on learning DNF's. CoRR Amit Daniely, Shai Shalev-Shwartz
2014	A*	`conf`	From average case complexity to improper learning complexity. STOC Amit Daniely, Nati Linial, Shai Shalev-Shwartz
2014	J	`jnl`	Inapproximability of Truthful Mechanisms via Generalizations of the VC Dimension. CoRR Amit Daniely, Michael Schapira, Gal Shahaf
2014	—	`conf`	Learning Economic Parameters from Revealed Preferences. WINE Maria-Florina Balcan, Amit Daniely, Ruta Mehta, Ruth Urner, Vijay V. Vazirani
2014	J	`jnl`	Learning Economic Parameters from Revealed Preferences. CoRR Maria-Florina Balcan, Amit Daniely, Ruta Mehta, Ruth Urner, Vijay V. Vazirani
2014	J	`jnl`	Optimal Learners for Multiclass Problems. CoRR Amit Daniely, Shai Shalev-Shwartz
2014	A*	`conf`	Optimal learners for multiclass problems. COLT Amit Daniely, Shai Shalev-Shwartz
2014	A*	`conf`	The Complexity of Learning Halfspaces using Generalized Linear Methods. COLT Amit Daniely, Nati Linial, Shai Shalev-Shwartz
2013	J	`jnl`	From average case complexity to improper learning complexity. CoRR Amit Daniely, Nati Linial, Shai Shalev-Shwartz
2013	—	`conf`	More data speeds up training time in learning halfspaces over sparse vectors. NIPS Amit Daniely, Nati Linial, Shai Shalev-Shwartz
2013	J	`jnl`	More data speeds up training time in learning halfspaces over sparse vectors. CoRR Amit Daniely, Nati Linial, Shai Shalev-Shwartz
2013	J	`jnl`	Multiclass learnability and the ERM principle. CoRR Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz
2013	A	`conf`	On the practically interesting instances of MAXCUT. STACS Yonatan Bilu, Amit Daniely, Nati Linial, Michael E. Saks
2013	J	`jnl`	The price of bandit information in multiclass online classification CoRR Amit Daniely, Tom Helbertal
2013	A*	`conf`	The price of bandit information in multiclass online classification. COLT Amit Daniely, Tom Helbertal
2012	J	`jnl`	Clustering is difficult only when it does not matter CoRR Amit Daniely, Nati Linial, Michael E. Saks
2012	J	`jnl`	Multiclass Learning Approaches: A Theoretical Comparison with Implications CoRR Amit Daniely, Sivan Sabato, Shai Shalev-Shwartz
2012	—	`conf`	Multiclass Learning Approaches: A Theoretical Comparison with Implications. NIPS Amit Daniely, Sivan Sabato, Shai Shalev-Shwartz
2012	J	`jnl`	On the practically interesting instances of MAXCUT CoRR Yonatan Bilu, Amit Daniely, Nati Linial, Michael E. Saks
2012	J	`jnl`	The error rate of learning halfspaces using Kernel-SVMs CoRR Amit Daniely, Nati Linial, Shai Shalev-Shwartz
2012	J	`jnl`	Tight products and graph expansion. J. Graph Theory Amit Daniely, Nathan Linial
2011	A*	`conf`	Multiclass Learnability and the ERM principle. COLT Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz
2010	J	`jnl`	Tight products and Expansion CoRR Amit Daniely, Nathan Linial