Shay Moran
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
72
Venues
14
Active years
2014–2026
Best venue rank
A*
Where they publish
Papers
72 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | COLT | Margin in Abstract Spaces. | Yair Ashlagi, Roi Livni, Shay Moran, Tom Waknine |
| 2026 | COLT | Learning from Equivalence Queries, Revisited. | Mark Braverman, Roi Livni, Yishay Mansour, Shay Moran, Kobbi Nissim |
| 2026 | COLT | Learning Conditional Averages. | Marco Bressan, Nataly Brukhim, Nicol Cesa-Bianchi, Emmanuel Esposito, Yishay Mansour, Shay Moran, Maximilian Thiessen |
| 2026 | COLT | The Sample Complexity of Multiclass and Sparse Contextual Bandits. | Liad Erez, Fan Chen, Alon Cohen, Tomer Koren, Yishay Mansour, Shay Moran, Alexander Rakhlin |
| 2026 | COLT | Optimal Reconstruction from Linear Queries. | Yuval Filmus, Shay Moran, Elizaveta Nesterova |
| 2026 | COLT | Uniform Laws of Large Numbers in Product Spaces. | Ron Holzman, Shay Moran, Alexander Shlimovich |
| 2026 | STOC | Sample Complexity of Agnostic Multiclass Classification: Natarajan Dimension Strikes Back. | Alon Cohen, Liad Erez, Steve Hanneke, Tomer Koren, Yishay Mansour, Shay Moran, Qian Zhang |
| 2026 | STOC | On the Learning Curves of Revenue Maximization. | Steve Hanneke, Alkis Kalavasis, Shay Moran, Grigoris Velegkas |
| 2025 | COLT | Of Dice and Games: A Theory of Generalized Boosting. | Marco Bressan, Nataly Brukhim, Nicol Cesa-Bianchi, Emmanuel Esposito, Yishay Mansour, Shay Moran, Maximilian Thiessen |
| 2025 | COLT | A Fine-grained Characterization of PAC Learnability. | Marco Bressan, Nataly Brukhim, Nicol Cesa-Bianchi, Emmanuel Esposito, Yishay Mansour, Shay Moran, Maximilian Thiessen |
| 2025 | COLT | Spherical Dimension. | Bogdan Chornomaz, Shay Moran, Tom Waknine |
| 2025 | COLT | Private List Learnability vs. Online List Learnability. | Steve Hanneke, Shay Moran, Hilla Schefler, Iska Tsubari |
| 2025 | COLT | Data Selection for ERMs. | Steve Hanneke, Shay Moran, Alexander Shlimovich, Amir Yehudayoff |
| 2025 | COLT | Open Problem: Data Selection for Regression Tasks. | Steve Hanneke, Shay Moran, Alexander Shlimovich, Amir Yehudayoff |
| 2025 | ICML | The Role of Randomness in Stability. | Max Hopkins, Shay Moran |
| 2025 | STOC | On Reductions and Representations of Learning Problems in Euclidean Spaces. | Bogdan Chornomaz, Shay Moran, Tom Waknine |
| 2025 | STOC | On Differentially Private Linear Algebra. | Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer, Nitzan Tur |
| 2024 | COLT | Dual VC Dimension Obstructs Sample Compression by Embeddings. | Zachary Chase, Bogdan Chornomaz, Steve Hanneke, Shay Moran, Amir Yehudayoff |
| 2024 | COLT | Learnability Gaps of Strategic Classification. | Lee Cohen, Yishay Mansour, Shay Moran, Han Shao |
| 2024 | COLT | A Theory of Interpretable Approximations. | Marco Bressan, Nicol Cesa-Bianchi, Emmanuel Esposito, Yishay Mansour, Shay Moran, Maximilian Thiessen |
| 2024 | COLT | A Unified Characterization of Private Learnability via Graph Theory. | Noga Alon, Shay Moran, Hilla Schefler, Amir Yehudayoff |
| 2024 | COLT | The Real Price of Bandit Information in Multiclass Classification. | Liad Erez, Alon Cohen, Tomer Koren, Yishay Mansour, Shay Moran |
| 2024 | COLT | List Sample Compression and Uniform Convergence. | Steve Hanneke, Shay Moran, Tom Waknine |
| 2024 | COLT | Open problem: Direct Sums in Learning Theory. | Steve Hanneke, Shay Moran, Tom Waknine |
| 2024 | FOCS | Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem. | Simone Fioravanti, Steve Hanneke, Shay Moran, Hilla Schefler, Iska Tsubari |
| 2024 | ICWSM | EnronSR: A Benchmark for Evaluating AI-Generated Email Replies. | Shay Moran, Roei Davidson, Nir Grinberg |
| 2024 | STOC | Local Borsuk-Ulam, Stability, and Replicability. | Zachary Chase, Bogdan Chornomaz, Shay Moran, Amir Yehudayoff |
| 2023 | COLT | Fine-Grained Distribution-Dependent Learning Curves. | Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin |
| 2023 | COLT | Improper Multiclass Boosting. | Nataly Brukhim, Steve Hanneke, Shay Moran |
| 2023 | COLT | Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension. | Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran |
| 2023 | COLT | Multiclass Online Learning and Uniform Convergence. | Steve Hanneke, Shay Moran, Vinod Raman, Unique Subedi, Ambuj Tewari |
| 2023 | COLT | Universal Rates for Multiclass Learning. | Steve Hanneke, Shay Moran, Qian Zhang |
| 2023 | COLT | List Online Classification. | Shay Moran, Ohad Sharon, Iska Tsubari, Sivan Yosebashvili |
| 2023 | FOCS | Stability and Replicability in Learning. | Zachary Chase, Shay Moran, Amir Yehudayoff |
| 2023 | ICML | Statistical Indistinguishability of Learning Algorithms. | Alkis Kalavasis, Amin Karbasi, Shay Moran, Grigoris Velegkas |
| 2022 | COLT | Monotone Learning. | Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer |
| 2022 | FOCS | A Characterization of Multiclass Learnability. | Nataly Brukhim, Daniel Carmon, Irit Dinur, Shay Moran, Amir Yehudayoff |
| 2022 | ICML | A Resilient Distributed Boosting Algorithm. | Yuval Filmus, Idan Mehalel, Shay Moran |
| 2022 | ISIT | Understanding Generalization via Leave-One-Out Conditional Mutual Information. | Mahdi Haghifam, Shay Moran, Daniel M. Roy, Gintare Karolina Dziugaite |
| 2022 | UAI | Active learning with label comparisons. | Gal Yona, Shay Moran, Gal Elidan, Amir Globerson |
| 2021 | COLT | Near Optimal Distributed Learning of Halfspaces with Two Parties. | Mark Braverman, Gillat Kol, Shay Moran, Raghuvansh R. Saxena |
| 2021 | COLT | Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games. | Steve Hanneke, Roi Livni, Shay Moran |
| 2021 | FOCS | A Theory of PAC Learnability of Partial Concept Classes. | Noga Alon, Steve Hanneke, Ron Holzman, Shay Moran |
| 2021 | FOCS | Statistically Near-Optimal Hypothesis Selection. | Olivier Bousquet, Mark Braverman, Gillat Kol, Klim Efremenko, Shay Moran |
| 2021 | STOC | Adversarial laws of large numbers and optimal regret in online classification. | Noga Alon, Omri Ben-Eliezer, Yuval Dagan, Shay Moran, Moni Naor, Eylon Yogev |
| 2021 | STOC | Boosting simple learners. | Noga Alon, Alon Gonen, Elad Hazan, Shay Moran |
| 2021 | STOC | Learnability can be independent of set theory (invited paper). | Shai Ben-David, Pavel Hrubes, Shay Moran, Amir Shpilka, Amir Yehudayoff |
| 2021 | STOC | A theory of universal learning. | Olivier Bousquet, Steve Hanneke, Shay Moran, Ramon van Handel, Amir Yehudayoff |
| 2020 | CiE | On the Perceptron's Compression. | Shay Moran, Ido Nachum, Itai Panasoff, Amir Yehudayoff |
| 2020 | COLT | Closure Properties for Private Classification and Online Prediction. | Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer |
| 2020 | COLT | Proper Learning, Helly Number, and an Optimal SVM Bound. | Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy |
| 2020 | FOCS | An Equivalence Between Private Classification and Online Prediction. | Mark Bun, Roi Livni, Shay Moran |
| 2020 | ICML | Private Query Release Assisted by Public Data. | Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, Zhiwei Steven Wu |
| 2019 | COLT | Private Center Points and Learning of Halfspaces. | Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer |
| 2019 | COLT | The Optimal Approximation Factor in Density Estimation. | Olivier Bousquet, Daniel Kane, Shay Moran |
| 2019 | COLT | On Communication Complexity of Classification Problems. | Daniel Kane, Roi Livni, Shay Moran, Amir Yehudayoff |
| 2019 | ICALP | Unlabeled Sample Compression Schemes and Corner Peelings for Ample and Maximum Classes. | Jrmie Chalopin, Victor Chepoi, Shay Moran, Manfred K. Warmuth |
| 2019 | STOC | Private PAC learning implies finite Littlestone dimension. | Noga Alon, Roi Livni, Maryanthe Malliaris, Shay Moran |
| 2018 | ALT | Learners that Use Little Information. | Raef Bassily, Shay Moran, Ido Nachum, Jonathan Shafer, Amir Yehudayoff |
| 2018 | ICALP | Generalized Comparison Trees for Point-Location Problems. | Daniel M. Kane, Shachar Lovett, Shay Moran |
| 2018 | STOC | Near-optimal linear decision trees for k-SUM and related problems. | Daniel M. Kane, Shachar Lovett, Shay Moran |
| 2017 | FOCS | Active Classification with Comparison Queries. | Daniel M. Kane, Shachar Lovett, Shay Moran, Jiapeng Zhang |
| 2017 | STOC | Twenty (simple) questions. | Yuval Dagan, Yuval Filmus, Ariel Gabizon, Shay Moran |
| 2016 | ALT | Labeled Compression Schemes for Extremal Classes. | Shay Moran, Manfred K. Warmuth |
| 2016 | COLT | Sign rank versus VC dimension. | Noga Alon, Shay Moran, Amir Yehudayoff |
| 2016 | ESA | Hitting Set for Hypergraphs of Low VC-dimension. | Karl Bringmann, Lszl Kozma, Shay Moran, N. S. Narayanaswamy |
| 2016 | ITA | Sample compression schemes for VC classes. | Shay Moran, Amir Yehudayoff |
| 2016 | MFCS | Shattered Sets and the Hilbert Function. | Shay Moran, Cyrus Rashtchian |
| 2016 | SIROCCO | Fooling Pairs in Randomized Communication Complexity. | Shay Moran, Makrand Sinha, Amir Yehudayoff |
| 2015 | ESA | Node-Balancing by Edge-Increments. | Friedrich Eisenbrand, Shay Moran, Rom Pinchasi, Martin Skutella |
| 2015 | FOCS | Compressing and Teaching for Low VC-Dimension. | Shay Moran, Amir Shpilka, Avi Wigderson, Amir Yehudayoff |
| 2014 | ICALP | Approximate Nonnegative Rank Is Equivalent to the Smooth Rectangle Bound. | Gillat Kol, Shay Moran, Amir Shpilka, Amir Yehudayoff |