Fast Fault Tolerant Rumor Spreading with Minimum Message Complexity - Benjamin Doerr; Shay Moran; Shlomo Moran; Carola Winzen (CoRR, 2012)

Shattering-Extremal Systems - Shay Moran (CoRR, 2012)

Shattering, Graph Orientations, and Connectivity. - László Kozma 002; Shay Moran (Electron. J. Comb., 2013)

Direct Sum Fails for Zero Error Average Communication. - Gillat Kol; Shay Moran; Amir Shpilka; Amir Yehudayoff (Electron. Colloquium Comput. Complex., 2013)

A note on average-case sorting. - Shay Moran; Amir Yehudayoff (Electron. Colloquium Comput. Complex., 2013)

Sign rank, VC dimension and spectral gaps. - Noga Alon; Shay Moran; Amir Yehudayoff (Electron. Colloquium Comput. Complex., 2014)

Internal compression of protocols to entropy. - Balthazar Bauer; Shay Moran; Amir Yehudayoff (Electron. Colloquium Comput. Complex., 2014)

Fooling Pairs in Randomized Communication Complexity. - Shay Moran; Makrand Sinha; Amir Yehudayoff (Electron. Colloquium Comput. Complex., 2014)

Node-Balancing by Edge-Increments. - Friedrich Eisenbrand; Shay Moran; Rom Pinchasi; Martin Skutella (ESA, 2015)

Compressing and Teaching for Low VC-Dimension. - Shay Moran; Amir Shpilka; Avi Wigderson; Amir Yehudayoff (FOCS, 2015)

Matchings vs hitting sets among half-spaces in low dimensional euclidean spaces. - Shay Moran; Rom Pinchasi (CoRR, 2015)

Shattered Sets and the Hilbert Function. - Shay Moran; Cyrus Rashtchian (Electron. Colloquium Comput. Complex., 2015)

Teaching and compressing for low VC-dimension. - Shay Moran; Amir Shpilka; Avi Wigderson; Amir Yehudayoff (Electron. Colloquium Comput. Complex., 2015)

Proper PAC learning is compressing. - Shay Moran; Amir Yehudayoff (Electron. Colloquium Comput. Complex., 2015)

Labeled Compression Schemes for Extremal Classes. - Shay Moran; Manfred K. Warmuth (ALT, 2016)

Sign rank versus VC dimension. - Noga Alon; Shay Moran; Amir Yehudayoff (COLT, 2016)

Hitting Set for Hypergraphs of Low VC-dimension. - Karl Bringmann; László Kozma 002; Shay Moran; N. S. Narayanaswamy (ESA, 2016)

Supervised learning through the lens of compression. - Ofir David; Shay Moran; Amir Yehudayoff (NIPS, 2016)

On statistical learning via the lens of compression. - Ofir David; Shay Moran; Amir Yehudayoff (CoRR, 2016)

Simple and optimal randomized fault-tolerant rumor spreading. - Benjamin Doerr; Carola Doerr; Shay Moran; Shlomo Moran (Distributed Comput., 2016)

Sample Compression Schemes for VC Classes. - Shay Moran; Amir Yehudayoff (J. ACM, 2016)

Generalization and simplification in machine learning. - Shay Moran (2016)

Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues. - Noga Alon; Moshe Babaioff; Yannai A. Gonczarowski; Yishay Mansour; Shay Moran; Amir Yehudayoff (NIPS, 2017)

Twenty (simple) questions. - Yuval Dagan; Yuval Filmus; Ariel Gabizon; Shay Moran (STOC, 2017)

Learners that Leak Little Information. - Raef Bassily; Shay Moran; Ido Nachum; Jonathan Shafer; Amir Yehudayoff (CoRR, 2017)

A learning problem that is independent of the set theory ZFC axioms. - Shai Ben-David; Pavel Hrubes; Shay Moran; Amir Shpilka; Amir Yehudayoff (CoRR, 2017)

Near-optimal linear decision trees for k-SUM and related problems. - Daniel M. Kane; Shachar Lovett; Shay Moran (Electron. Colloquium Comput. Complex., 2017)

On Communication Complexity of Classification Problems. - Daniel M. Kane; Roi Livni; Shay Moran; Amir Yehudayoff (Electron. Colloquium Comput. Complex., 2017)

Active classification with comparison queries. - Daniel M. Kane; Shachar Lovett; Shay Moran; Jiapeng Zhang (Electron. Colloquium Comput. Complex., 2017)

Learners that Use Little Information. - Raef Bassily; Shay Moran; Ido Nachum; Jonathan Shafer; Amir Yehudayoff (ALT, 2018)

Are Two (Samples) Really Better Than One? - Moshe Babaioff; Yannai A. Gonczarowski; Yishay Mansour; Shay Moran (EC, 2018)

A Sauer-Shelah-Perles Lemma for Sumsets. - Zeev Dvir; Shay Moran (Electron. J. Comb., 2018)

Are Two (Samples) Really Better Than One? On the Non-Asymptotic Performance of Empirical Revenue Maximization. - Moshe Babaioff; Yannai A. Gonczarowski; Yishay Mansour; Shay Moran (CoRR, 2018)

Generalized comparison trees for point-location problems. - Daniel M. Kane; Shachar Lovett; Shay Moran (Electron. Colloquium Comput. Complex., 2018)

Private Center Points and Learning of Halfspaces. - Amos Beimel; Shay Moran; Kobbi Nissim; Uri Stemmer (COLT, 2019)

The Optimal Approximation Factor in Density Estimation. - Olivier Bousquet; Daniel Kane 001; Shay Moran (COLT, 2019)

On Weak epsilon-Nets and the Radon Number. - Shay Moran; Amir Yehudayoff (SoCG, 2019)

An adaptive nearest neighbor rule for classification. - Akshay Balsubramani; Sanjoy Dasgupta; Yoav Freund; Shay Moran (NeurIPS, 2019)

Limits of Private Learning with Access to Public Data. - Raef Bassily; Shay Moran; Noga Alon (NeurIPS, 2019)

Learning to Screen. - Alon Cohen; Avinatan Hassidim; Haim Kaplan; Yishay Mansour; Shay Moran (NeurIPS, 2019)

Private Learning Implies Online Learning: An Efficient Reduction. - Alon Gonen; Elad Hazan; Shay Moran (NeurIPS, 2019)

Private PAC learning implies finite Littlestone dimension. - Noga Alon; Roi Livni; Maryanthe Malliaris; Shay Moran (STOC, 2019)

Approximate Nonnegative Rank is Equivalent to the Smooth Rectangle Bound. - Gillat Kol; Shay Moran; Amir Shpilka; Amir Yehudayoff (Comput. Complex., 2019)

Twenty (Short) Questions. - Yuval Dagan; Yuval Filmus; Ariel Gabizon; Shay Moran (Comb., 2019)

Passing Tests without Memorizing: Two Models for Fooling Discriminators. - Olivier Bousquet; Roi Livni; Shay Moran (CoRR, 2019)

Learning and Generalization for Matching Problems. - Alon Cohen; Avinatan Hassidim; Haim Kaplan; Yishay Mansour; Shay Moran (CoRR, 2019)

Convex Set Disjointness, Distributed Learning of Halfspaces, and LP Feasibility. - Mark Braverman; Gillat Kol; Shay Moran; Raghuvansh R. Saxena (CoRR, 2019)

Learnability can be undecidable. - Shai Ben-David; Pavel Hrubes; Shay Moran; Amir Shpilka; Amir Yehudayoff (Nat. Mach. Intell., 2019)

Author Correction: Learnability can be undecidable. - Shai Ben-David; Pavel Hrubes; Shay Moran; Amir Shpilka; Amir Yehudayoff (Nat. Mach. Intell., 2019)

On the Perceptron's Compression. - Shay Moran; Ido Nachum; Itai Panasoff; Amir Yehudayoff (CiE, 2020)

Closure Properties for Private Classification and Online Prediction. - Noga Alon; Amos Beimel; Shay Moran; Uri Stemmer (COLT, 2020)

Proper Learning, Helly Number, and an Optimal SVM Bound. - Olivier Bousquet; Steve Hanneke; Shay Moran; Nikita Zhivotovskiy (COLT, 2020)

An Equivalence Between Private Classification and Online Prediction. - Mark Bun; Roi Livni; Shay Moran (FOCS, 2020)

Private Query Release Assisted by Public Data. - Raef Bassily; Albert Cheu; Shay Moran; Aleksandar Nikolov; Jonathan R. Ullman; Zhiwei Steven Wu (ICML, 2020)

Learning from Mixtures of Private and Public Populations. - Raef Bassily; Shay Moran; Anupama Nandi (NeurIPS, 2020)

Synthetic Data Generators - Sequential and Private. - Olivier Bousquet; Roi Livni; Shay Moran (NeurIPS, 2020)

Online Agnostic Boosting via Regret Minimization. - Nataly Brukhim; Xinyi Chen 001; Elad Hazan; Shay Moran (NeurIPS, 2020)

A Limitation of the PAC-Bayes Framework. - Roi Livni; Shay Moran (NeurIPS, 2020)

A Sauer-Shelah-Perles Lemma for Lattices. - Stijn Cambie; Bogdan Chornomaz; Zeev Dvir; Yuval Filmus; Shay Moran (Electron. J. Comb., 2020)

On the Information Complexity of Proper Learners for VC Classes in the Realizable Case. - Mahdi Haghifam; Gintare Karolina Dziugaite; Shay Moran; Daniel M. Roy 001 (CoRR, 2020)

On Weak ε-Nets and the Radon Number. - Shay Moran; Amir Yehudayoff (Discret. Comput. Geom., 2020)

Near Optimal Distributed Learning of Halfspaces with Two Parties. - Mark Braverman; Gillat Kol; Shay Moran; Raghuvansh R. Saxena (COLT, 2021)

Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games. - Steve Hanneke; Roi Livni; Shay Moran (COLT, 2021)

A Theory of PAC Learnability of Partial Concept Classes. - Noga Alon; Steve Hanneke; Ron Holzman; Shay Moran (FOCS, 2021)

Statistically Near-Optimal Hypothesis Selection. - Olivier Bousquet; Mark Braverman; Gillat Kol; Klim Efremenko; Shay Moran (FOCS, 2021)

The Entropy of Lies: Playing Twenty Questions with a Liar. - Yuval Dagan; Yuval Filmus; Daniel Kane 001; Shay Moran (ITCS, 2021)

Multiclass Boosting and the Cost of Weak Learning. - Nataly Brukhim; Elad Hazan; Shay Moran; Indraneel Mukherjee; Robert E. Schapire (NeurIPS, 2021)

Towards a Unified Information-Theoretic Framework for Generalization. - Mahdi Haghifam; Gintare Karolina Dziugaite; Shay Moran; Daniel M. Roy 001 (NeurIPS, 2021)

Adversarial laws of large numbers and optimal regret in online classification. - Noga Alon; Omri Ben-Eliezer; Yuval Dagan; Shay Moran; Moni Naor; Eylon Yogev (STOC, 2021)

Learnability can be independent of set theory (invited paper). - Shai Ben-David; Pavel Hrubes; Shay Moran; Amir Shpilka; Amir Yehudayoff (STOC, 2021)

A theory of universal learning. - Olivier Bousquet; Steve Hanneke; Shay Moran; Ramon van Handel; Amir Yehudayoff (STOC, 2021)

Agnostic Online Learning and Excellent Sets. - Maryanthe Malliaris; Shay Moran (CoRR, 2021)

Elementary Derivations of the Euclidean Hurwitz Algebras Adapted from Gadi Moran's last paper. - Tomer Moran; Shay Moran; Shlomo Moran (Am. Math. Mon., 2021)

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

A Resilient Distributed Boosting Algorithm. - Yuval Filmus; Idan Mehalel; Shay Moran (ICML, 2022)

Uniform Brackets, Containers, and Combinatorial Macbeath Regions. - Kunal Dutta; Arijit Ghosh; Shay Moran (ITCS, 2022)

Understanding Generalization via Leave-One-Out Conditional Mutual Information. - Mahdi Haghifam; Shay Moran; Daniel M. Roy 001; Gintare Karolina Dziugaite (ISIT, 2022)

Integral Probability Metrics PAC-Bayes Bounds. - Ron Amit; Baruch Epstein; Shay Moran; Ron Meir (NeurIPS, 2022)

On Optimal Learning Under Targeted Data Poisoning. - Steve Hanneke; Amin Karbasi; Mohammad Mahmoody; Idan Mehalel; Shay Moran (NeurIPS, 2022)

Universal Rates for Interactive Learning. - Steve Hanneke; Amin Karbasi; Shay Moran; Grigoris Velegkas (NeurIPS, 2022)

Active learning with label comparisons. - Gal Yona; Shay Moran; Gal Elidan; Amir Globerson (UAI, 2022)

How Expressive Are Friendly School Partitions? - Josef Minarík; Shay Moran; Michael Skotnica (CoRR, 2022)

Differentially-Private Bayes Consistency. - Olivier Bousquet; Haim Kaplan; Aryeh Kontorovich; Yishay Mansour; Shay Moran; Menachem Sadigurschi; Uri Stemmer (CoRR, 2022)

The unstable formula theorem revisited. - Maryanthe Malliaris; Shay Moran (CoRR, 2022)

A Characterization of Multiclass Learnability. - Nataly Brukhim; Daniel Carmon; Irit Dinur; Shay Moran; Amir Yehudayoff (Electron. Colloquium Comput. Complex., 2022)

Private and Online Learnability Are Equivalent. - Noga Alon; Mark Bun; Roi Livni; Maryanthe Malliaris; Shay Moran (J. ACM, 2022)

Unlabeled sample compression schemes and corner peelings for ample and maximum classes. - Jérémie Chalopin; Victor Chepoi; Shay Moran; Manfred K. Warmuth (J. Comput. Syst. Sci., 2022)

Fine-Grained Distribution-Dependent Learning Curves. - Olivier Bousquet; Steve Hanneke; Shay Moran; Jonathan Shafer; Ilya O. Tolstikhin (COLT, 2023)

Improper Multiclass Boosting. - Nataly Brukhim; Steve Hanneke; Shay Moran (COLT, 2023)

Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension. - Yuval Filmus; Steve Hanneke; Idan Mehalel; Shay Moran (COLT, 2023)

Multiclass Online Learning and Uniform Convergence. - Steve Hanneke; Shay Moran; Vinod Raman; Unique Subedi; Ambuj Tewari (COLT, 2023)

Universal Rates for Multiclass Learning. - Steve Hanneke; Shay Moran; Qian Zhang 067 (COLT, 2023)

List Online Classification. - Shay Moran; Ohad Sharon; Iska Tsubari; Sivan Yosebashvili (COLT, 2023)

Stability and Replicability in Learning. - Zachary Chase 001; Shay Moran; Amir Yehudayoff (FOCS, 2023)

Statistical Indistinguishability of Learning Algorithms. - Alkis Kalavasis; Amin Karbasi; Shay Moran; Grigoris Velegkas (ICML, 2023)

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

Adversarial Resilience in Sequential Prediction via Abstention. - Surbhi Goel; Steve Hanneke; Shay Moran; Abhishek Shetty (NeurIPS, 2023)

A Trichotomy for Transductive Online Learning. - Steve Hanneke; Shay Moran; Jonathan Shafer (NeurIPS, 2023)

Black-Box Differential Privacy for Interactive ML. - Haim Kaplan; Yishay Mansour; Shay Moran; Kobbi Nissim; Uri Stemmer (NeurIPS, 2023)

The Bayesian Stability Zoo. - Shay Moran; Hilla Schefler; Jonathan Shafer (NeurIPS, 2023)

On Differentially Private Online Predictions. - Haim Kaplan; Yishay Mansour; Shay Moran; Kobbi Nissim; Uri Stemmer (CoRR, 2023)

List Online Classification. - Shay Moran; Ohad Sharon; Iska Tsubari (CoRR, 2023)

Replicability and stability in learning. - Zachary Chase 001; Shay Moran; Amir Yehudayoff (CoRR, 2023)

Boosting Simple Learners. - Noga Alon; Alon Gonen; Elad Hazan; Shay Moran (TheoretiCS, 2023)

Dual VC Dimension Obstructs Sample Compression by Embeddings. - Zachary Chase 001; Bogdan Chornomaz; Steve Hanneke; Shay Moran; Amir Yehudayoff (COLT, 2024)

Learnability Gaps of Strategic Classification. - Lee Cohen 001; Yishay Mansour; Shay Moran; Han Shao (COLT, 2024)

A Theory of Interpretable Approximations. - Marco Bressan 002; Nicolò Cesa-Bianchi; Emmanuel Esposito; Yishay Mansour; Shay Moran; Maximilian Thiessen (COLT, 2024)

A Unified Characterization of Private Learnability via Graph Theory. - Noga Alon; Shay Moran; Hilla Schefler; Amir Yehudayoff (COLT, 2024)

The Real Price of Bandit Information in Multiclass Classification. - Liad Erez; Alon Cohen; Tomer Koren; Yishay Mansour; Shay Moran (COLT, 2024)

List Sample Compression and Uniform Convergence. - Steve Hanneke; Shay Moran; Tom Waknine (COLT, 2024)

Open problem: Direct Sums in Learning Theory. - Steve Hanneke; Shay Moran; Tom Waknine (COLT, 2024)

Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem. - Simone Fioravanti; Steve Hanneke; Shay Moran; Hilla Schefler; Iska Tsubari (FOCS, 2024)

Can Copyright Be Reduced to Privacy? - Niva Elkin-Koren; Uri Hacohen; Roi Livni; Shay Moran (FORC, 2024)

EnronSR: A Benchmark for Evaluating AI-Generated Email Replies. - Shay Moran; Roei Davidson; Nir Grinberg (ICWSM, 2024)

Learning-Augmented Algorithms with Explicit Predictors. - Marek Eliás 001; Haim Kaplan; Yishay Mansour; Shay Moran (NeurIPS, 2024)

Fast Rates for Bandit PAC Multiclass Classification. - Liad Erez; Alon Peled-Cohen; Tomer Koren; Yishay Mansour; Shay Moran (NeurIPS, 2024)

Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs. - Yuval Filmus; Steve Hanneke; Idan Mehalel; Shay Moran (NeurIPS, 2024)

Universal Rates for Active Learning. - Steve Hanneke; Amin Karbasi; Shay Moran; Grigoris Velegkas (NeurIPS, 2024)

Improved Sample Complexity for Multiclass PAC Learning. - Steve Hanneke; Shay Moran; Qian Zhang 067 (NeurIPS, 2024)

Credit Attribution and Stable Compression. - Roi Livni; Shay Moran; Kobbi Nissim; Chirag Pabbaraju (NeurIPS, 2024)

Local Borsuk-Ulam, Stability, and Replicability. - Zachary Chase 001; Bogdan Chornomaz; Shay Moran; Amir Yehudayoff (STOC, 2024)

Probably Approximately Precision and Recall Learning. - Lee Cohen 001; Yishay Mansour; Shay Moran; Han Shao (CoRR, 2024)

On the ERM Principle in Meta-Learning. - Yannay Alon; Steve Hanneke; Shay Moran; Uri Shalit (CoRR, 2024)

Diagonalization Games. - Noga Alon; Olivier Bousquet; Kasper Green Larsen; Shay Moran; Shlomo Moran (Am. Math. Mon., 2024)

Of Dice and Games: A Theory of Generalized Boosting. - Marco Bressan 002; Nataly Brukhim; Nicolò Cesa-Bianchi; Emmanuel Esposito; Yishay Mansour; Shay Moran; Maximilian Thiessen (COLT, 2025)

A Fine-grained Characterization of PAC Learnability. - Marco Bressan 002; Nataly Brukhim; Nicolò Cesa-Bianchi; Emmanuel Esposito; Yishay Mansour; Shay Moran; Maximilian Thiessen (COLT, 2025)

Spherical Dimension. - Bogdan Chornomaz; Shay Moran; Tom Waknine (COLT, 2025)

Private List Learnability vs. Online List Learnability. - Steve Hanneke; Shay Moran; Hilla Schefler; Iska Tsubari (COLT, 2025)

Data Selection for ERMs. - Steve Hanneke; Shay Moran; Alexander Shlimovich; Amir Yehudayoff (COLT, 2025)

Open Problem: Data Selection for Regression Tasks. - Steve Hanneke; Shay Moran; Alexander Shlimovich; Amir Yehudayoff (COLT, 2025)

Data Reconstruction: When You See It and When You Don't. - Edith Cohen; Haim Kaplan; Yishay Mansour; Shay Moran; Kobbi Nissim; Uri Stemmer; Eliad Tsfadia (ITCS, 2025)

On Reductions and Representations of Learning Problems in Euclidean Spaces. - Bogdan Chornomaz; Shay Moran; Tom Waknine (STOC, 2025)

On Differentially Private Linear Algebra. - Haim Kaplan; Yishay Mansour; Shay Moran; Uri Stemmer; Nitzan Tur (STOC, 2025)

The unstable formula theorem revisited via algorithms. - Maryanthe Malliaris; Shay Moran (Ann. Pure Appl. Log., 2025)

The Role of Randomness in Stability. - Max Hopkins; Shay Moran (CoRR, 2025)

Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness. - Bogdan Chornomaz; Yonatan Koren; Shay Moran; Tom Waknine (CoRR, 2025)