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2020 – today
- 2024
[c81]Noga Alon, Shay Moran, Hilla Schefler, Amir Yehudayoff:
A Unified Characterization of Private Learnability via Graph Theory. COLT 2024: 94-129- [c80]Marco Bressan, Nicolò Cesa-Bianchi, Emmanuel Esposito, Yishay Mansour, Shay Moran, Maximilian Thiessen:
A Theory of Interpretable Approximations. COLT 2024: 648-668 - [c79]Zachary Chase, Bogdan Chornomaz, Steve Hanneke, Shay Moran, Amir Yehudayoff:
Dual VC Dimension Obstructs Sample Compression by Embeddings. COLT 2024: 923-946 - [c78]Lee Cohen, Yishay Mansour, Shay Moran, Han Shao:
Learnability Gaps of Strategic Classification. COLT 2024: 1223-1259 - [c77]Liad Erez, Alon Cohen, Tomer Koren, Yishay Mansour, Shay Moran:
The Real Price of Bandit Information in Multiclass Classification. COLT 2024: 1573-1598 - [c76]Steve Hanneke, Shay Moran, Tom Waknine:
List Sample Compression and Uniform Convergence. COLT 2024: 2360-2388 - [c75]Steve Hanneke, Shay Moran, Tom Waknine:
Open problem: Direct Sums in Learning Theory. COLT 2024: 5325-5329 - [c74]Niva Elkin-Koren, Uri Hacohen, Roi Livni, Shay Moran:
Can Copyright Be Reduced to Privacy? FORC 2024: 3:1-3:18 - [c73]Shay Moran, Roei Davidson, Nir Grinberg:
EnronSR: A Benchmark for Evaluating AI-Generated Email Replies. ICWSM 2024: 2063-2075 - [c72]Zachary Chase
, Bogdan Chornomaz, Shay Moran, Amir Yehudayoff:
Local Borsuk-Ulam, Stability, and Replicability. STOC 2024: 1769-1780 - [i102]Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran:
Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs. CoRR abs/2402.07453 (2024) - [i101]Lee Cohen, Yishay Mansour, Shay Moran, Han Shao:
Learnability Gaps of Strategic Classification. CoRR abs/2402.19303 (2024) - [i100]Marek Eliás, Haim Kaplan, Yishay Mansour, Shay Moran:
Learning-Augmented Algorithms with Explicit Predictors. CoRR abs/2403.07413 (2024) - [i99]Steve Hanneke, Shay Moran, Tom Waknine:
List Sample Compression and Uniform Convergence. CoRR abs/2403.10889 (2024) - [i98]Liad Erez, Alon Cohen, Tomer Koren, Yishay Mansour, Shay Moran:
The Real Price of Bandit Information in Multiclass Classification. CoRR abs/2405.10027 (2024) - [i97]Edith Cohen, Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer, Eliad Tsfadia:
Data Reconstruction: When You See It and When You Don't. CoRR abs/2405.15753 (2024) - [i96]Zachary Chase, Bogdan Chornomaz, Steve Hanneke, Shay Moran, Amir Yehudayoff:
Dual VC Dimension Obstructs Sample Compression by Embeddings. CoRR abs/2405.17120 (2024) - [i95]Marco Bressan, Nicolò Cesa-Bianchi, Emmanuel Esposito, Yishay Mansour, Shay Moran, Maximilian Thiessen:
A Theory of Interpretable Approximations. CoRR abs/2406.10529 (2024) - [i94]Liad Erez, Alon Cohen, Tomer Koren, Yishay Mansour, Shay Moran:
Fast Rates for Bandit PAC Multiclass Classification. CoRR abs/2406.12406 (2024) - [i93]Roi Livni, Shay Moran, Kobbi Nissim, Chirag Pabbaraju:
Credit Attribution and Stable Compression. CoRR abs/2406.15916 (2024) - [i92]Simone Fioravanti, Steve Hanneke, Shay Moran, Hilla Schefler, Iska Tsubari:
Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem. CoRR abs/2407.07765 (2024) - 2023
- [j17]Noga Alon, Alon Gonen, Elad Hazan, Shay Moran:
Boosting Simple Learners. TheoretiCS 2 (2023) - [c71]Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran:
Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension. COLT 2023: 773-836 - [c70]Shay Moran, Ohad Sharon, Iska Tsubari, Sivan Yosebashvili:
List Online Classification. COLT 2023: 1885-1913 - [c69]Nataly Brukhim, Steve Hanneke, Shay Moran:
Improper Multiclass Boosting. COLT 2023: 5433-5452 - [c68]Steve Hanneke, Shay Moran, Qian Zhang:
Universal Rates for Multiclass Learning. COLT 2023: 5615-5681 - [c67]Steve Hanneke, Shay Moran, Vinod Raman, Unique Subedi, Ambuj Tewari:
Multiclass Online Learning and Uniform Convergence. COLT 2023: 5682-5696 - [c66]Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin:
Fine-Grained Distribution-Dependent Learning Curves. COLT 2023: 5890-5924 - [c65]Zachary Chase, Shay Moran, Amir Yehudayoff:
Stability and Replicability in Learning. FOCS 2023: 2430-2439 - [c64]Alkis Kalavasis, Amin Karbasi, Shay Moran, Grigoris Velegkas:
Statistical Indistinguishability of Learning Algorithms. ICML 2023: 15586-15622 - [c63]Nataly Brukhim, Amit Daniely, Yishay Mansour, Shay Moran:
Multiclass Boosting: Simple and Intuitive Weak Learning Criteria. NeurIPS 2023 - [c62]Surbhi Goel, Steve Hanneke, Shay Moran, Abhishek Shetty:
Adversarial Resilience in Sequential Prediction via Abstention. NeurIPS 2023 - [c61]Steve Hanneke, Shay Moran, Jonathan Shafer:
A Trichotomy for Transductive Online Learning. NeurIPS 2023 - [c60]Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer:
Black-Box Differential Privacy for Interactive ML. NeurIPS 2023 - [c59]Shay Moran, Hilla Schefler, Jonathan Shafer:
The Bayesian Stability Zoo. NeurIPS 2023 - [i91]Noga Alon, Olivier Bousquet, Kasper Green Larsen, Shay Moran, Shlomo Moran:
Diagonalization Games. CoRR abs/2301.01924 (2023) - [i90]Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran:
Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension. CoRR abs/2302.13849 (2023) - [i89]Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer:
On Differentially Private Online Predictions. CoRR abs/2302.14099 (2023) - [i88]Shay Moran, Ohad Sharon, Iska Tsubari:
List Online Classification. CoRR abs/2303.15383 (2023) - [i87]Zachary Chase, Shay Moran, Amir Yehudayoff:
Replicability and stability in learning. CoRR abs/2304.03757 (2023) - [i86]Noga Alon, Shay Moran, Hilla Schefler, Amir Yehudayoff:
A Unified Characterization of Private Learnability via Graph Theory. CoRR abs/2304.03996 (2023) - [i85]Alkis Kalavasis, Amin Karbasi, Shay Moran, Grigoris Velegkas:
Statistical Indistinguishability of Learning Algorithms. CoRR abs/2305.14311 (2023) - [i84]Niva Elkin-Koren, Uri Hacohen, Roi Livni, Shay Moran:
Can Copyright be Reduced to Privacy? CoRR abs/2305.14822 (2023) - [i83]Surbhi Goel, Steve Hanneke, Shay Moran, Abhishek Shetty:
Adversarial Resilience in Sequential Prediction via Abstention. CoRR abs/2306.13119 (2023) - [i82]Nataly Brukhim, Amit Daniely, Yishay Mansour, Shay Moran:
Multiclass Boosting: Simple and Intuitive Weak Learning Criteria. CoRR abs/2307.00642 (2023) - [i81]Steve Hanneke, Shay Moran, Qian Zhang:
Universal Rates for Multiclass Learning. CoRR abs/2307.02066 (2023) - [i80]Shay Moran, Hilla Schefler, Jonathan Shafer:
The Bayesian Stability Zoo. CoRR abs/2310.18428 (2023) - [i79]Zachary Chase, Bogdan Chornomaz, Shay Moran, Amir Yehudayoff:
Local Borsuk-Ulam, Stability, and Replicability. CoRR abs/2311.01599 (2023) - [i78]Steve Hanneke, Shay Moran, Jonathan Shafer:
A Trichotomy for Transductive Online Learning. CoRR abs/2311.06428 (2023) - [i77]Noga Alon, Olivier Bousquet, Kasper Green Larsen, Shay Moran, Shlomo Moran:
Diagonalization Games. Electron. Colloquium Comput. Complex. TR23 (2023) - 2022
- [j16]Noga Alon, Mark Bun, Roi Livni, Maryanthe Malliaris, Shay Moran:
Private and Online Learnability Are Equivalent. J. ACM 69(4): 28:1-28:34 (2022) - [j15]Jérémie Chalopin, Victor Chepoi, Shay Moran, Manfred K. Warmuth:
Unlabeled sample compression schemes and corner peelings for ample and maximum classes. J. Comput. Syst. Sci. 127: 1-28 (2022) - [c58]Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer:
Monotone Learning. COLT 2022: 842-866 - [c57]Nataly Brukhim, Daniel Carmon, Irit Dinur, Shay Moran, Amir Yehudayoff:
A Characterization of Multiclass Learnability. FOCS 2022: 943-955 - [c56]Yuval Filmus, Idan Mehalel, Shay Moran:
A Resilient Distributed Boosting Algorithm. ICML 2022: 6465-6473 - [c55]Kunal Dutta, Arijit Ghosh, Shay Moran:
Uniform Brackets, Containers, and Combinatorial Macbeath Regions. ITCS 2022: 59:1-59:10 - [c54]Mahdi Haghifam, Shay Moran, Daniel M. Roy, Gintare Karolina Dziugaite:
Understanding Generalization via Leave-One-Out Conditional Mutual Information. ISIT 2022: 2487-2492 - [c53]Ron Amit, Baruch Epstein, Shay Moran, Ron Meir:
Integral Probability Metrics PAC-Bayes Bounds. NeurIPS 2022 - [c52]Steve Hanneke, Amin Karbasi, Mohammad Mahmoody, Idan Mehalel, Shay Moran:
On Optimal Learning Under Targeted Data Poisoning. NeurIPS 2022 - [c51]Steve Hanneke, Amin Karbasi, Shay Moran, Grigoris Velegkas:
Universal Rates for Interactive Learning. NeurIPS 2022 - [c50]Gal Yona, Shay Moran, Gal Elidan, Amir Globerson:
Active learning with label comparisons. UAI 2022: 2289-2298 - [i76]Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer:
Monotone Learning. CoRR abs/2202.05246 (2022) - [i75]Nataly Brukhim, Daniel Carmon, Irit Dinur, Shay Moran, Amir Yehudayoff:
A Characterization of Multiclass Learnability. CoRR abs/2203.01550 (2022) - [i74]Josef Minarík, Shay Moran, Michael Skotnica:
How Expressive Are Friendly School Partitions? CoRR abs/2203.10772 (2022) - [i73]Gal Yona, Shay Moran, Gal Elidan, Amir Globerson:
Active Learning with Label Comparisons. CoRR abs/2204.04670 (2022) - [i72]Yuval Filmus, Idan Mehalel, Shay Moran:
A Resilient Distributed Boosting Algorithm. CoRR abs/2206.04713 (2022) - [i71]Mahdi Haghifam, Shay Moran, Daniel M. Roy, Gintare Karolina Dziugaite:
Understanding Generalization via Leave-One-Out Conditional Mutual Information. CoRR abs/2206.14800 (2022) - [i70]Ron Amit, Baruch Epstein, Shay Moran, Ron Meir:
Integral Probability Metrics PAC-Bayes Bounds. CoRR abs/2207.00614 (2022) - [i69]Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin:
Fine-Grained Distribution-Dependent Learning Curves. CoRR abs/2208.14615 (2022) - [i68]Steve Hanneke, Amin Karbasi, Mohammad Mahmoody, Idan Mehalel, Shay Moran:
On Optimal Learning Under Targeted Data Poisoning. CoRR abs/2210.02713 (2022) - [i67]Olivier Bousquet, Haim Kaplan, Aryeh Kontorovich, Yishay Mansour, Shay Moran, Menachem Sadigurschi, Uri Stemmer:
Differentially-Private Bayes Consistency. CoRR abs/2212.04216 (2022) - [i66]Maryanthe Malliaris, Shay Moran:
The unstable formula theorem revisited. CoRR abs/2212.05050 (2022) - [i65]Nataly Brukhim, Daniel Carmon, Irit Dinur, Shay Moran, Amir Yehudayoff:
A Characterization of Multiclass Learnability. Electron. Colloquium Comput. Complex. TR22 (2022) - 2021
- [j14]Tomer Moran, Shay Moran, Shlomo Moran:
Elementary Derivations of the Euclidean Hurwitz Algebras Adapted from Gadi Moran's last paper. Am. Math. Mon. 128(8): 726-736 (2021) - [c49]Mark Braverman, Gillat Kol, Shay Moran, Raghuvansh R. Saxena:
Near Optimal Distributed Learning of Halfspaces with Two Parties. COLT 2021: 724-758 - [c48]Steve Hanneke, Roi Livni, Shay Moran:
Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games. COLT 2021: 2289-2314 - [c47]Noga Alon, Steve Hanneke, Ron Holzman, Shay Moran:
A Theory of PAC Learnability of Partial Concept Classes. FOCS 2021: 658-671 - [c46]Olivier Bousquet, Mark Braverman, Gillat Kol, Klim Efremenko, Shay Moran:
Statistically Near-Optimal Hypothesis Selection. FOCS 2021: 909-919 - [c45]Yuval Dagan, Yuval Filmus, Daniel Kane, Shay Moran:
The Entropy of Lies: Playing Twenty Questions with a Liar. ITCS 2021: 1:1-1:16 - [c44]Nataly Brukhim, Elad Hazan, Shay Moran, Indraneel Mukherjee, Robert E. Schapire:
Multiclass Boosting and the Cost of Weak Learning. NeurIPS 2021: 3057-3067 - [c43]Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Daniel M. Roy:
Towards a Unified Information-Theoretic Framework for Generalization. NeurIPS 2021: 26370-26381 - [c42]Shai Ben-David, Pavel Hrubes, Shay Moran, Amir Shpilka, Amir Yehudayoff:
Learnability can be independent of set theory (invited paper). STOC 2021: 11 - [c41]Noga Alon, Omri Ben-Eliezer, Yuval Dagan, Shay Moran, Moni Naor, Eylon Yogev:
Adversarial laws of large numbers and optimal regret in online classification. STOC 2021: 447-455 - [c40]Noga Alon, Alon Gonen, Elad Hazan, Shay Moran:
Boosting simple learners. STOC 2021: 481-489 - [c39]Olivier Bousquet, Steve Hanneke, Shay Moran, Ramon van Handel, Amir Yehudayoff:
A theory of universal learning. STOC 2021: 532-541 - [i64]Noga Alon, Omri Ben-Eliezer, Yuval Dagan, Shay Moran, Moni Naor, Eylon Yogev:
Adversarial Laws of Large Numbers and Optimal Regret in Online Classification. CoRR abs/2101.09054 (2021) - [i63]Steve Hanneke, Roi Livni, Shay Moran:
Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games. CoRR abs/2102.01646 (2021) - [i62]Noga Alon, Steve Hanneke, Ron Holzman, Shay Moran:
A Theory of PAC Learnability of Partial Concept Classes. CoRR abs/2107.08444 (2021) - [i61]Maryanthe Malliaris, Shay Moran:
Agnostic Online Learning and Excellent Sets. CoRR abs/2108.05569 (2021) - [i60]Olivier Bousquet, Mark Braverman, Klim Efremenko, Gillat Kol, Shay Moran:
Statistically Near-Optimal Hypothesis Selection. CoRR abs/2108.07880 (2021) - [i59]Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Daniel M. Roy:
Towards a Unified Information-Theoretic Framework for Generalization. CoRR abs/2111.05275 (2021) - [i58]Kunal Dutta, Arijit Ghosh, Shay Moran:
Uniform Brackets, Containers, and Combinatorial Macbeath Regions. CoRR abs/2111.10048 (2021) - 2020
- [j13]Stijn Cambie, Bogdan Chornomaz, Zeev Dvir, Yuval Filmus, Shay Moran:
A Sauer-Shelah-Perles Lemma for Lattices. Electron. J. Comb. 27(4): 4 (2020) - [j12]Shay Moran, Amir Yehudayoff:
On Weak ε-Nets and the Radon Number. Discret. Comput. Geom. 64(4): 1125-1140 (2020) - [c38]Shay Moran, Ido Nachum, Itai Panasoff, Amir Yehudayoff:
On the Perceptron's Compression. CiE 2020: 310-325 - [c37]Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer:
Closure Properties for Private Classification and Online Prediction. COLT 2020: 119-152 - [c36]Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy:
Proper Learning, Helly Number, and an Optimal SVM Bound. COLT 2020: 582-609 - [c35]Mark Bun, Roi Livni, Shay Moran:
An Equivalence Between Private Classification and Online Prediction. FOCS 2020: 389-402 - [c34]Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, Zhiwei Steven Wu:
Private Query Release Assisted by Public Data. ICML 2020: 695-703 - [c33]Raef Bassily, Shay Moran, Anupama Nandi:
Learning from Mixtures of Private and Public Populations. NeurIPS 2020 - [c32]Olivier Bousquet, Roi Livni, Shay Moran:
Synthetic Data Generators - Sequential and Private. NeurIPS 2020 - [c31]Nataly Brukhim, Xinyi Chen, Elad Hazan, Shay Moran:
Online Agnostic Boosting via Regret Minimization. NeurIPS 2020 - [c30]Roi Livni, Shay Moran:
A Limitation of the PAC-Bayes Framework. NeurIPS 2020 - [i57]Noga Alon, Alon Gonen, Elad Hazan, Shay Moran:
Boosting Simple Learners. CoRR abs/2001.11704 (2020) - [i56]Mark Bun, Roi Livni, Shay Moran:
An Equivalence Between Private Classification and Online Prediction. CoRR abs/2003.00563 (2020) - [i55]Nataly Brukhim, Xinyi Chen, Elad Hazan, Shay Moran:
Online Agnostic Boosting via Regret Minimization. CoRR abs/2003.01150 (2020) - [i54]Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer:
Closure Properties for Private Classification and Online Prediction. CoRR abs/2003.04509 (2020) - [i53]Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, Zhiwei Steven Wu:
Private Query Release Assisted by Public Data. CoRR abs/2004.10941 (2020) - [i52]Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy:
Proper Learning, Helly Number, and an Optimal SVM Bound. CoRR abs/2005.11818 (2020) - [i51]Roi Livni, Shay Moran:
A Limitation of the PAC-Bayes Framework. CoRR abs/2006.13508 (2020) - [i50]Raef Bassily, Shay Moran, Anupama Nandi:
Learning from Mixtures of Private and Public Populations. CoRR abs/2008.00331 (2020) - [i49]Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Daniel M. Roy:
On the Information Complexity of Proper Learners for VC Classes in the Realizable Case. CoRR abs/2011.02970 (2020) - [i48]Olivier Bousquet, Steve Hanneke, Shay Moran, Ramon van Handel, Amir Yehudayoff:
A Theory of Universal Learning. CoRR abs/2011.04483 (2020)
2010 – 2019
- 2019
- [j11]Gillat Kol, Shay Moran, Amir Shpilka, Amir Yehudayoff:
Approximate Nonnegative Rank is Equivalent to the Smooth Rectangle Bound. Comput. Complex. 28(1): 1-25 (2019) - [j10]Yuval Dagan, Yuval Filmus, Ariel Gabizon, Shay Moran:
Twenty (Short) Questions. Comb. 39(3): 597-626 (2019) - [j9]Daniel M. Kane, Shachar Lovett, Shay Moran:
Near-optimal Linear Decision Trees for k-SUM and Related Problems. J. ACM 66(3): 16:1-16:18 (2019) - [j8]Shai Ben-David, Pavel Hrubes, Shay Moran, Amir Shpilka, Amir Yehudayoff:
Learnability can be undecidable. Nat. Mach. Intell. 1(1): 44-48 (2019) - [j7]Shai Ben-David, Pavel Hrubes, Shay Moran, Amir Shpilka, Amir Yehudayoff:
Author Correction: Learnability can be undecidable. Nat. Mach. Intell. 1(2): 121 (2019) - [c29]Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer:
Private Center Points and Learning of Halfspaces. COLT 2019: 269-282 - [c28]Olivier Bousquet, Daniel Kane, Shay Moran:
The Optimal Approximation Factor in Density Estimation. COLT 2019: 318-341 - [c27]Daniel Kane, Roi Livni, Shay Moran, Amir Yehudayoff:
On Communication Complexity of Classification Problems. COLT 2019: 1903-1943 - [c26]Shay Moran, Amir Yehudayoff:
On Weak epsilon-Nets and the Radon Number. SoCG 2019: 51:1-51:14 - [c25]Jérémie Chalopin, Victor Chepoi, Shay Moran, Manfred K. Warmuth:
Unlabeled Sample Compression Schemes and Corner Peelings for Ample and Maximum Classes. ICALP 2019: 34:1-34:15 - [c24]Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund, Shay Moran:
An adaptive nearest neighbor rule for classification. NeurIPS 2019: 7577-7586 - [c23]Alon Cohen, Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Shay Moran:
Learning to Screen. NeurIPS 2019: 8612-8621 - [c22]Alon Gonen, Elad Hazan, Shay Moran:
Private Learning Implies Online Learning: An Efficient Reduction. NeurIPS 2019: 8699-8709 - [c21]Raef Bassily, Shay Moran, Noga Alon:
Limits of Private Learning with Access to Public Data. NeurIPS 2019: 10342-10352 - [c20]Noga Alon, Roi Livni, Maryanthe Malliaris, Shay Moran:
Private PAC learning implies finite Littlestone dimension. STOC 2019: 852-860 - [i47]Olivier Bousquet, Roi Livni, Shay Moran:
Passing Tests without Memorizing: Two Models for Fooling Discriminators. CoRR abs/1902.03468 (2019) - [i46]Alon Cohen, Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Shay Moran:
Learning and Generalization for Matching Problems. CoRR abs/1902.04741 (2019) - [i45]Olivier Bousquet, Daniel Kane, Shay Moran:
The Optimal Approximation Factor in Density Estimation. CoRR abs/1902.05876 (2019) - [i44]Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer:
Private Center Points and Learning of Halfspaces. CoRR abs/1902.10731 (2019) - [i43]Alon Gonen, Elad Hazan, Shay Moran:
Private Learning Implies Online Learning: An Efficient Reduction. CoRR abs/1905.11311 (2019) - [i42]Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund, Shay Moran:
An adaptive nearest neighbor rule for classification. CoRR abs/1905.12717 (2019) - [i41]Mark Braverman, Gillat Kol, Shay Moran, Raghuvansh R. Saxena:
Convex Set Disjointness, Distributed Learning of Halfspaces, and LP Feasibility. CoRR abs/1909.03547 (2019) - [i40]Noga Alon, Raef Bassily, Shay Moran:
Limits of Private Learning with Access to Public Data. CoRR abs/1910.11519 (2019) - 2018
- [j6]Zeev Dvir, Shay Moran:
A Sauer-Shelah-Perles Lemma for Sumsets. Electron. J. Comb. 25(4): 4 (2018) - [c19]Raef Bassily, Shay Moran, Ido Nachum, Jonathan Shafer, Amir Yehudayoff:
Learners that Use Little Information. ALT 2018: 25-55 - [c18]Daniel M. Kane, Shachar Lovett, Shay Moran:
Generalized Comparison Trees for Point-Location Problems. ICALP 2018: 82:1-82:13 - [c17]Moshe Babaioff, Yannai A. Gonczarowski, Yishay Mansour, Shay Moran:
Are Two (Samples) Really Better Than One? EC 2018: 175 - [c16]Daniel M. Kane, Shachar Lovett, Shay Moran:
Near-optimal linear decision trees for k-SUM and related problems. STOC 2018: 554-563 - [i39]Moshe Babaioff, Yannai A. Gonczarowski, Yishay Mansour, Shay Moran:
Are Two (Samples) Really Better Than One? On the Non-Asymptotic Performance of Empirical Revenue Maximization. CoRR abs/1802.08037 (2018) - [i38]Daniel M. Kane, Shachar Lovett, Shay Moran:
Generalized comparison trees for point-location problems. CoRR abs/1804.08237 (2018) - [i37]Noga Alon, Roi Livni, Maryanthe Malliaris, Shay Moran:
Private PAC learning implies finite Littlestone dimension. CoRR abs/1806.00949 (2018) - [i36]Shay Moran, Ido Nachum, Itai Panasoff, Amir Yehudayoff:
On the Perceptron's Compression. CoRR abs/1806.05403 (2018) - [i35]Zeev Dvir, Shay Moran:
A Sauer-Shelah-Perles Lemma for Sumsets. CoRR abs/1806.05737 (2018) - [i34]Yuval Dagan, Yuval Filmus, Daniel Kane, Shay Moran:
The entropy of lies: playing twenty questions with a liar. CoRR abs/1811.02177 (2018) - [i33]Jérémie Chalopin, Victor Chepoi, Shay Moran, Manfred K. Warmuth:
Unlabeled sample compression schemes and corner peelings for ample and maximum classes. CoRR abs/1812.02099 (2018) - [i32]Daniel M. Kane, Shachar Lovett, Shay Moran:
Generalized comparison trees for point-location problems. Electron. Colloquium Comput. Complex. TR18 (2018) - 2017
- [c15]Daniel M. Kane, Shachar Lovett, Shay Moran, Jiapeng Zhang:
Active Classification with Comparison Queries. FOCS 2017: 355-366 - [c14]Noga Alon, Moshe Babaioff, Yannai A. Gonczarowski, Yishay Mansour, Shay Moran, Amir Yehudayoff:
Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues. NIPS 2017: 1656-1665 - [c13]Yuval Dagan, Yuval Filmus, Ariel Gabizon, Shay Moran:
Twenty (simple) questions. STOC 2017: 9-21 - [i31]Daniel M. Kane, Shachar Lovett, Shay Moran, Jiapeng Zhang:
Active classification with comparison queries. CoRR abs/1704.03564 (2017) - [i30]Daniel M. Kane, Shachar Lovett, Shay Moran:
Near-optimal linear decision trees for k-SUM and related problems. CoRR abs/1705.01720 (2017) - [i29]Noga Alon, Moshe Babaioff, Yannai A. Gonczarowski, Yishay Mansour, Shay Moran, Amir Yehudayoff:
Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues. CoRR abs/1705.08430 (2017) - [i28]Shay Moran, Amir Yehudayoff:
On weak $ε$-nets and the Radon number. CoRR abs/1707.05381 (2017) - [i27]Raef Bassily, Shay Moran, Ido Nachum, Jonathan Shafer, Amir Yehudayoff:
Learners that Leak Little Information. CoRR abs/1710.05233 (2017) - [i26]Shai Ben-David, Pavel Hrubes, Shay Moran, Amir Shpilka, Amir Yehudayoff:
A learning problem that is independent of the set theory ZFC axioms. CoRR abs/1711.05195 (2017) - [i25]Daniel M. Kane, Roi Livni, Shay Moran, Amir Yehudayoff:
On Communication Complexity of Classification Problems. CoRR abs/1711.05893 (2017) - [i24]Daniel M. Kane, Shachar Lovett, Shay Moran:
Near-optimal linear decision trees for k-SUM and related problems. Electron. Colloquium Comput. Complex. TR17 (2017) - [i23]Daniel M. Kane, Roi Livni, Shay Moran, Amir Yehudayoff:
On Communication Complexity of Classification Problems. Electron. Colloquium Comput. Complex. TR17 (2017) - [i22]Daniel M. Kane, Shachar Lovett, Shay Moran, Jiapeng Zhang:
Active classification with comparison queries. Electron. Colloquium Comput. Complex. TR17 (2017) - 2016
- [b1]Shay Moran:
Generalization and simplification in machine learning. Technion - Israel Institute of Technology, Israel, 2016 - [j5]Gillat Kol, Shay Moran, Amir Shpilka, Amir Yehudayoff:
Direct Sum Fails for Zero-Error Average Communication. Algorithmica 76(3): 782-795 (2016) - [j4]Benjamin Doerr, Carola Doerr, Shay Moran, Shlomo Moran:
Simple and optimal randomized fault-tolerant rumor spreading. Distributed Comput. 29(2): 89-104 (2016) - [j3]Shay Moran, Amir Yehudayoff:
Sample Compression Schemes for VC Classes. J. ACM 63(3): 21:1-21:10 (2016) - [j2]Shay Moran, Amir Yehudayoff:
A Note on Average-Case Sorting. Order 33(1): 23-28 (2016) - [c12]Shay Moran, Manfred K. Warmuth:
Labeled Compression Schemes for Extremal Classes. ALT 2016: 34-49 - [c11]Noga Alon, Shay Moran, Amir Yehudayoff:
Sign rank versus VC dimension. COLT 2016: 47-80 - [c10]Karl Bringmann, László Kozma, Shay Moran, N. S. Narayanaswamy:
Hitting Set for Hypergraphs of Low VC-dimension. ESA 2016: 23:1-23:18 - [c9]Shay Moran, Amir Yehudayoff:
Sample compression schemes for VC classes. ITA 2016: 1-14 - [c8]Shay Moran, Cyrus Rashtchian:
Shattered Sets and the Hilbert Function. MFCS 2016: 70:1-70:14 - [c7]Ofir David, Shay Moran, Amir Yehudayoff:
Supervised learning through the lens of compression. NIPS 2016: 2784-2792 - [c6]Shay Moran, Makrand Sinha, Amir Yehudayoff:
Fooling Pairs in Randomized Communication Complexity. SIROCCO 2016: 49-59 - [i21]Ofir David, Shay Moran, Amir Yehudayoff:
On statistical learning via the lens of compression. CoRR abs/1610.03592 (2016) - [i20]Yuval Dagan, Yuval Filmus, Ariel Gabizon, Shay Moran:
Twenty (simple) questions. CoRR abs/1611.01655 (2016) - 2015
- [c5]Balthazar Bauer, Shay Moran, Amir Yehudayoff:
Internal Compression of Protocols to Entropy. APPROX-RANDOM 2015: 481-496 - [c4]Friedrich Eisenbrand, Shay Moran, Rom Pinchasi, Martin Skutella:
Node-Balancing by Edge-Increments. ESA 2015: 450-458 - [c3]Shay Moran, Amir Shpilka, Avi Wigderson, Amir Yehudayoff:
Compressing and Teaching for Low VC-Dimension. FOCS 2015: 40-51 - [i19]Shay Moran, Amir Shpilka, Avi Wigderson, Amir Yehudayoff:
Teaching and compressing for low VC-dimension. CoRR abs/1502.06187 (2015) - [i18]Shay Moran, Amir Yehudayoff:
Proper PAC learning is compressing. CoRR abs/1503.06960 (2015) - [i17]Friedrich Eisenbrand, Shay Moran, Rom Pinchasi, Martin Skutella:
Node-balancing by edge-increments. CoRR abs/1504.06919 (2015) - [i16]Shay Moran, Manfred K. Warmuth:
Labeled compression schemes for extremal classes. CoRR abs/1506.00165 (2015) - [i15]Shay Moran, Rom Pinchasi:
Matchings vs hitting sets among half-spaces in low dimensional euclidean spaces. CoRR abs/1507.02504 (2015) - [i14]Shay Moran, Cyrus Rashtchian:
Shattered Sets and the Hilbert Function. CoRR abs/1511.08245 (2015) - [i13]Karl Bringmann, László Kozma, Shay Moran, N. S. Narayanaswamy:
Hitting Set in hypergraphs of low VC-dimension. CoRR abs/1512.00481 (2015) - [i12]Shay Moran, Cyrus Rashtchian:
Shattered Sets and the Hilbert Function. Electron. Colloquium Comput. Complex. TR15 (2015) - [i11]Shay Moran, Amir Shpilka, Avi Wigderson, Amir Yehudayoff:
Teaching and compressing for low VC-dimension. Electron. Colloquium Comput. Complex. TR15 (2015) - [i10]Shay Moran, Amir Yehudayoff:
Proper PAC learning is compressing. Electron. Colloquium Comput. Complex. TR15 (2015) - 2014
- [c2]Gillat Kol, Shay Moran, Amir Shpilka, Amir Yehudayoff:
Approximate Nonnegative Rank Is Equivalent to the Smooth Rectangle Bound. ICALP (1) 2014: 701-712 - [c1]Gillat Kol, Shay Moran, Amir Shpilka, Amir Yehudayoff:
Direct sum fails for zero error average communication. ITCS 2014: 517-522 - [i9]Noga Alon, Shay Moran, Amir Yehudayoff:
Sign rank, VC dimension and spectral gaps. Electron. Colloquium Comput. Complex. TR14 (2014) - [i8]Balthazar Bauer, Shay Moran, Amir Yehudayoff:
Internal compression of protocols to entropy. Electron. Colloquium Comput. Complex. TR14 (2014) - [i7]Gillat Kol, Shay Moran, Amir Shpilka, Amir Yehudayoff:
Approximate Nonnegative Rank is Equivalent to the Smooth Rectangle Bound. Electron. Colloquium Comput. Complex. TR14 (2014) - [i6]Shay Moran, Makrand Sinha, Amir Yehudayoff:
Fooling Pairs in Randomized Communication Complexity. Electron. Colloquium Comput. Complex. TR14 (2014) - 2013
- [j1]László Kozma, Shay Moran:
Shattering, Graph Orientations, and Connectivity. Electron. J. Comb. 20(3): 44 (2013) - [i5]Gillat Kol, Shay Moran, Amir Shpilka, Amir Yehudayoff:
Direct Sum Fails for Zero Error Average Communication. Electron. Colloquium Comput. Complex. TR13 (2013) - [i4]Shay Moran, Amir Yehudayoff:
A note on average-case sorting. Electron. Colloquium Comput. Complex. TR13 (2013) - 2012
- [i3]Benjamin Doerr, Shay Moran, Shlomo Moran, Carola Winzen:
Fast Fault Tolerant Rumor Spreading with Minimum Message Complexity. CoRR abs/1209.6158 (2012) - [i2]László Kozma, Shay Moran:
Shattering, Graph Orientations, and Connectivity. CoRR abs/1211.1319 (2012) - [i1]Shay Moran:
Shattering-Extremal Systems. CoRR abs/1211.2980 (2012)
Coauthor Index
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