Tight products and Expansion - Amit Daniely; Nathan Linial (CoRR, 2010)

Multiclass Learnability and the ERM principle. - Amit Daniely; Sivan Sabato; Shai Ben-David; Shai Shalev-Shwartz (COLT, 2011)

Multiclass Learning Approaches: A Theoretical Comparison with Implications. - Amit Daniely; Sivan Sabato; Shai Shalev-Shwartz (NIPS, 2012)

Clustering is difficult only when it does not matter - Amit Daniely; Nati Linial; Michael E. Saks (CoRR, 2012)

The error rate of learning halfspaces using Kernel-SVMs - Amit Daniely; Nati Linial; Shai Shalev-Shwartz (CoRR, 2012)

Tight products and graph expansion. - Amit Daniely; Nathan Linial (J. Graph Theory, 2012)

The price of bandit information in multiclass online classification. - Amit Daniely; Tom Helbertal (COLT, 2013)

More data speeds up training time in learning halfspaces over sparse vectors. - Amit Daniely; Nati Linial; Shai Shalev-Shwartz (NIPS, 2013)

On the practically interesting instances of MAXCUT. - Yonatan Bilu; Amit Daniely; Nati Linial; Michael E. Saks (STACS, 2013)

The Complexity of Learning Halfspaces using Generalized Linear Methods. - Amit Daniely; Nati Linial; Shai Shalev-Shwartz (COLT, 2014)

Optimal learners for multiclass problems. - Amit Daniely; Shai Shalev-Shwartz (COLT, 2014)

From average case complexity to improper learning complexity. - Amit Daniely; Nati Linial; Shai Shalev-Shwartz (STOC, 2014)

Learning Economic Parameters from Revealed Preferences. - Maria-Florina Balcan; Amit Daniely; Ruta Mehta; Ruth Urner; Vijay V. Vazirani (WINE, 2014)

A PTAS for Agnostically Learning Halfspaces. - Amit Daniely (COLT, 2015)

Strongly Adaptive Online Learning. - Amit Daniely; Alon Gonen; Shai Shalev-Shwartz (ICML, 2015)

Inapproximability of Truthful Mechanisms via Generalizations of the VC Dimension. - Amit Daniely; Michael Schapira; Gal Shahaf (STOC, 2015)

Complexity Theoretic Limitations on Learning DNF's. - Amit Daniely; Shai Shalev-Shwartz (COLT, 2016)

Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity. - Amit Daniely; Roy Frostig; Yoram Singer (NIPS, 2016)

Complexity theoretic limitations on learning halfspaces. - Amit Daniely (STOC, 2016)

Sketching and Neural Networks. - Amit Daniely; Nevena Lazic; Yoram Singer; Kunal Talwar (CoRR, 2016)

Behavior-Based Machine-Learning: A Hybrid Approach for Predicting Human Decision Making. - Gali Noti; Effi Levi; Yoav Kolumbus; Amit Daniely (CoRR, 2016)

Depth Separation for Neural Networks. - Amit Daniely (COLT, 2017)

Short and Deep: Sketching and Neural Networks. - Amit Daniely; Nevena Lazic; Yoram Singer; Kunal Talwar (ICLR (Workshop), 2017)

SGD Learns the Conjugate Kernel Class of the Network. - Amit Daniely (NIPS, 2017)

Random Features for Compositional Kernels. - Amit Daniely; Roy Frostig; Vineet Gupta 001; Yoram Singer (CoRR, 2017)

Learning with Rules. - Deborah Cohen; Amit Daniely; Amir Globerson; Gal Elidan (CoRR, 2018)

Planning and Learning with Stochastic Action Sets. - Craig Boutilier; Alon Cohen; Amit Daniely; Avinatan Hassidim; Yishay Mansour; Ofer Meshi; Martin Mladenov; Dale Schuurmans (CoRR, 2018)

Learning without Interaction Requires Separation. - Amit Daniely; Vitaly Feldman (CoRR, 2018)

Inapproximability of Truthful Mechanisms via Generalizations of the Vapnik-Chervonenkis Dimension. - Amit Daniely; Michael Schapira; Gal Shahaf (SIAM J. Comput., 2018)

Learning Rules-First Classifiers. - Deborah Cohen; Amit Daniely; Amir Globerson; Gal Elidan (AISTATS, 2019)

Competitive ratio vs regret minimization: achieving the best of both worlds. - Amit Daniely; Yishay Mansour (ALT, 2019)

Open Problem: Is Margin Sufficient for Non-Interactive Private Distributed Learning? - Amit Daniely; Vitaly Feldman (COLT, 2019)

Locally Private Learning without Interaction Requires Separation. - Amit Daniely; Vitaly Feldman (NeurIPS, 2019)

Generalization Bounds for Neural Networks via Approximate Description Length. - Amit Daniely; Elad Granot (NeurIPS, 2019)

Competitive ratio versus regret minimization: achieving the best of both worlds. - Amit Daniely; Yishay Mansour (CoRR, 2019)

On the Optimality of Trees Generated by ID3. - Alon Brutzkus; Amit Daniely; Eran Malach (CoRR, 2019)

Distribution Free Learning with Local Queries. - Galit Bary-Weisberg; Amit Daniely; Shai Shalev-Shwartz (ALT, 2020)

ID3 Learns Juntas for Smoothed Product Distributions. - Alon Brutzkus; Amit Daniely; Eran Malach (COLT, 2020)

The Implicit Bias of Depth: How Incremental Learning Drives Generalization. - Daniel Gissin; Shai Shalev-Shwartz; Amit Daniely (ICLR, 2020)

Neural Networks Learning and Memorization with (almost) no Over-Parameterization. - Amit Daniely (NeurIPS, 2020)

Learning Parities with Neural Networks. - Amit Daniely; Eran Malach (NeurIPS, 2020)

Most ReLU Networks Suffer from $ ell^2$ Adversarial Perturbations. - Amit Daniely; Hadas Shacham (NeurIPS, 2020)

Hardness of Learning Neural Networks with Natural Weights. - Amit Daniely; Gal Vardi (NeurIPS, 2020)

On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions. - Yossi Arjevani; Amit Daniely; Stefanie Jegelka; Hongzhou Lin (CoRR, 2020)

Memorizing Gaussians with no over-parameterizaion via gradient decent on neural networks. - Amit Daniely (CoRR, 2020)

Most ReLU Networks Suffer from 𝓁2 Adversarial Perturbations. - Amit Daniely; Hadas Schacham (CoRR, 2020)

From Local Pseudorandom Generators to Hardness of Learning. - Amit Daniely; Gal Vardi (COLT, 2021)

Asynchronous Stochastic Optimization Robust to Arbitrary Delays. - Alon Cohen; Amit Daniely; Yoel Drori; Tomer Koren; Mariano Schain (NeurIPS, 2021)

An Exact Poly-Time Membership-Queries Algorithm for Extraction a three-Layer ReLU Network. - Amit Daniely; Elad Granot (CoRR, 2021)

Monotone Learning. - Olivier Bousquet; Amit Daniely; Haim Kaplan; Yishay Mansour; Shay Moran; Uri Stemmer (COLT, 2022)

Approximate Description Length, Covering Numbers, and VC Dimension. - Amit Daniely; Gal Katzhendler (CoRR, 2022)

An Exact Poly-Time Membership-Queries Algorithm for Extracting a Three-Layer ReLU Network. - Amit Daniely; Elad Granot (ICLR, 2023)

Multiclass Boosting: Simple and Intuitive Weak Learning Criteria. - Nataly Brukhim; Amit Daniely; Yishay Mansour; Shay Moran (NeurIPS, 2023)

Most Neural Networks Are Almost Learnable. - Amit Daniely; Nati Srebro; Gal Vardi (NeurIPS, 2023)

Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy. - Amit Daniely; Nati Srebro; Gal Vardi (NeurIPS, 2023)

Efficiently Learning Neural Networks: What Assumptions May Suffice? - Amit Daniely; Nathan Srebro; Gal Vardi (CoRR, 2023)

On the Sample Complexity of Two-Layer Networks: Lipschitz Vs. Element-Wise Lipschitz Activation. - Amit Daniely; Elad Granot (ALT, 2024)

RedEx: Beyond Fixed Representation Methods via Convex Optimization. - Amit Daniely; Mariano Schain; Gilad Yehudai (ALT, 2024)

Locally Optimal Descent for Dynamic Stepsize Scheduling. - Gilad Yehudai; Alon Cohen; Amit Daniely; Yoel Drori; Tomer Koren; Mariano Schain (AISTATS, 2025)

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $ mathbb{SO}(d)$. - Amit Daniely (COLT, 2025)

Online Learning of Neural Networks. - Amit Daniely; Idan Mehalel; Elchanan Mossel (CoRR, 2025)

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on so(d). - Amit Daniely (CoRR, 2025)