| 2025 | AISTATS | Locally Optimal Descent for Dynamic Stepsize Scheduling. | Gilad Yehudai, Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain |
| 2025 | COLT | Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{SO}(d)$. | Amit Daniely |
| 2024 | ALT | On the Sample Complexity of Two-Layer Networks: Lipschitz Vs. Element-Wise Lipschitz Activation. | Amit Daniely, Elad Granot |
| 2024 | ALT | RedEx: Beyond Fixed Representation Methods via Convex Optimization. | Amit Daniely, Mariano Schain, Gilad Yehudai |
| 2023 | ICLR | An Exact Poly-Time Membership-Queries Algorithm for Extracting a Three-Layer ReLU Network. | Amit Daniely, Elad Granot |
| 2022 | COLT | Monotone Learning. | Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer |
| 2021 | COLT | From Local Pseudorandom Generators to Hardness of Learning. | Amit Daniely, Gal Vardi |
| 2020 | ALT | Distribution Free Learning with Local Queries. | Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz |
| 2020 | COLT | ID3 Learns Juntas for Smoothed Product Distributions. | Alon Brutzkus, Amit Daniely, Eran Malach |
| 2020 | ICLR | The Implicit Bias of Depth: How Incremental Learning Drives Generalization. | Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely |
| 2019 | AISTATS | Learning Rules-First Classifiers. | Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan |
| 2019 | ALT | Competitive ratio vs regret minimization: achieving the best of both worlds. | Amit Daniely, Yishay Mansour |
| 2019 | COLT | Open Problem: Is Margin Sufficient for Non-Interactive Private Distributed Learning? | Amit Daniely, Vitaly Feldman |
| 2017 | COLT | Depth Separation for Neural Networks. | Amit Daniely |
| 2017 | ICLR | Short and Deep: Sketching and Neural Networks. | Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar |
| 2016 | COLT | Complexity Theoretic Limitations on Learning DNF's. | Amit Daniely, Shai Shalev-Shwartz |
| 2016 | STOC | Complexity theoretic limitations on learning halfspaces. | Amit Daniely |
| 2015 | COLT | A PTAS for Agnostically Learning Halfspaces. | Amit Daniely |
| 2015 | ICML | Strongly Adaptive Online Learning. | Amit Daniely, Alon Gonen, Shai Shalev-Shwartz |
| 2015 | STOC | Inapproximability of Truthful Mechanisms via Generalizations of the VC Dimension. | Amit Daniely, Michael Schapira, Gal Shahaf |
| 2014 | COLT | The Complexity of Learning Halfspaces using Generalized Linear Methods. | Amit Daniely, Nati Linial, Shai Shalev-Shwartz |
| 2014 | COLT | Optimal learners for multiclass problems. | Amit Daniely, Shai Shalev-Shwartz |
| 2014 | STOC | From average case complexity to improper learning complexity. | Amit Daniely, Nati Linial, Shai Shalev-Shwartz |
| 2013 | COLT | The price of bandit information in multiclass online classification. | Amit Daniely, Tom Helbertal |
| 2013 | STACS | On the practically interesting instances of MAXCUT. | Yonatan Bilu, Amit Daniely, Nati Linial, Michael E. Saks |