Technical Reports (Under Review):
(8) A. Geva and A. Painsky
"Imposing Monotonicity Improves Large-Alphabet Distribution Estimation", Under Review
(7) I. Guy and A. Painsky
"Generalized Laplace Missing Mass Estimation", Under Review
(6) M. Sokolik and A. Painsky
"Estimating the Number of Rare Events with Applications to Large Alphabet Inference", Under Review
(5) A. Colombi, M. Beraha, A. Painsky and S. Favaro
Under Review [link]
(4) M. Molochny and A. Painsky
"Bootstrap Stacking for Small Validation Sets", Under Review
(3) A. Painsky
"Who is the Winning Algorithm? Rank Aggregation for Comparative Studies", Under Review [link]
(2) A. Painsky
"Inference of the Most Frequent Events", Under Review
(1) A. Painsky
"Near-optimal Inference of the Best Performing Algorithm", Under Review [link]
Journal Papers:
(28) A. Kontorovich and A. Painsky
"Distribution Estimation under the Infinity Norm"
Journal of Machine Learning Research (JMLR), accepted July 2025. To Appear [link]
(27) S. Anuk, T. Bendory and A. Painsky,
"Image Detection using Combinatorial Auction",
IEEE Open Journal of Signal Processing, Vol 5, pp. 1015 - 1022, Aug 2024 [link]
(26) R. Feng, S. Kim and A. Painsky
"Tokenization of Distributed Insurance by Auction",
Japanese Journal of Statistics and Data Science, Vol. 7, pp. 1039–1057, Jul 2024 [link]
(25) A. Pinchas, Irad Ben-Gal and A. Painsky,
"A Comparative Analysis of Discrete Entropy Estimators for Large Alphabet Problems",
Entropy, Special Issue on Information Theory for Data Science, Vol. 26(5), Apr 2024 [link]
(24) Y. Nissenbaum and A. Painsky,
"Cross-validated Tree-based Models for Multi-target Learning",
Frontiers in Artificial Intelligence, Vol. 7, Jan 2024 [link]
(23) A. Painsky,
"Confidence Intervals for Parameters of Unobserved Events",
Journal of the American Statistical Association (JASA), Vol. 120, Issue 549, pp. 226-236, Mar 2024 [link]
(22) D. Marton and A. Painsky,
"Good-Bootstrap: Simultaneous Confidence Intervals for Large Alphabet Distributions",
Journal of Nonparametric Statistics, Vol. 36, Issue 4, pp. 1177 - 1191, Mar 2024 [link]
(21) A. Painsky,
Information and Inference. Vol. 12, Issue 4, pp. 3067- 3086, Dec 2023 [link]
(20) Y. Eppel, M. Kaspi and A. Painsky,
"Decision Making for Basketball Clutch Shots: A Data Driven Approach",
Journal of Sports Analytics, Vol. 9, Issue. 3, pp. 245 - 259, Nov 2023 [link]
(19) M. Roth, A. Painsky and T. Bendory,
"Detecting Non-overlapping Signals with Dynamic Programming",
Entropy, Special Issue on Statistical Methods for Modeling High-Dimensional and Complex Data, Jan 2023 [link]
(18) M. Yechezkel, M. Mofaz, A. Painsky, T. Patalon, S. Gazit, E. Shmueli and D. Yamin,
"Safety of the Forth Covid-19 BNT162b2 mRNA (second booster) Vaccine: Prospective and Retrospective Cohort Study",
The Lancet Repository Medicine (IF 102.6), Oct 2022 [link]
(17) A. Painsky,
"Convergence Guarantees for the Good-Turing Estimator",
Journal of Machine Learning Research (JMLR), Vol 23, Issue 27, Sep 2022 [link]
(16) Y. Shalev, A. Painsky and I. Ben-Gal,
"Neural Joint Entropy Estimation",
IEEE Transactions on Neural Networks and Learning Systems, Vol. 35, Issue 4, pp. 5488 - 5500, Sep 2022 [link]
(15) A. Adler and A. Painsky,
"Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection",
Entropy, Special Issue on Statistical Methods for Complex Systems, May 2022 [link]
(14) A. Painsky,
"Generalized Good-Turing Improves Missing Mass Estimation",
Journal of the American Statistical Association (JASA), Jan 2022 [link]
(13) S. Rosset, R. Heller, A. Painsky and E. Aharoni,
"Optimal and Maximin Procedures for Multiple Testing Problems",
Journal of the Royal Statistical Society: Series B, Apr 2022 [link]
(12) A. Painsky and M. Feder
"Robust Universal Inference",
Entropy, Special Issue on Application of Information Theory in Statistics,
Vol 23, Issue 6, Jun 2021 [link] [Awarded Editor's Choice Article]
(11) A. Painsky, M. Feder and N. Tishby,
"Non-linear Canonical Correlation Analysis: A Compressed Representation Approach",
Entropy, Special Issue on Theory and Applications of Information Theoretic Machine Learning,
Vol 22, Issue, 2, Feb 2020 [link]
(10) A. Painsky and G. W. Wornell,
"Bregman Divergence Bounds and Universality Properties of the Logarithmic Loss",
IEEE Transactions on Information Theory, Vol, 66, Issue 3, Mar 2020 [link]
(9) A. Painsky, S. Rosset and M. Feder,
"Innovation Representation with Application to Causal Inference",
IEEE Transactions on Information Theory, Vol. 66, Issue 2, Feb 2020 [link]
(8) A. Painsky and S. Rosset,
"Lossless Compression of Random Forests",
Journal of Computer Science and Technology, Vol. 34, Issue 2, Mar 2019 [link]
(7) A. Painsky, S. Rosset and M. Feder,
"Linear Independent Component Analysis over Finite Fields: Algorithms and Bounds",
IEEE Transactions on Signal Processing, Vol. 66, Issue 22, Nov 2018 [link]
(6) A. Painsky and N. Tishby,
"Gaussian Lower Bound for the Information Bottleneck Limit",
Journal of Machine Learning Research (JMLR), Vol. 18, Issue 1, Apr 2018 [link]
(5) A. Painsky, S. Rosset and M. Feder,
"Large Alphabet Source Coding using Independent Component Analysis",
IEEE Transactions on Information Theory, Vol. 63, Issue 10, Oct 2017 [link]
(4) A. Painsky and S. Rosset,
"Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance",
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol. 39, Issue 11, Dec 2016 [link]
(3) A. Painsky, S. Rosset and M. Feder,
"Generalized Independent Component Analysis over Finite Alphabets",
IEEE Transactions on Information Theory, Vol. 62, Issue 2, Feb 2016 [link]
(2) A. Painsky and S. Rosset,
"Isotonic Modeling with Non-differentiable Loss Functions with Application to Lasso Regularization",
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol. 38, Issue 2, Feb 2016 [link]
(1) A. Painsky and S. Rosset,
"Optimal Set Cover Formulation for Exclusive Row Biclustering of Gene Expression",
Journal of Computer Science and Technology, Vol. 29, Issue 3, Apr 2013 [link]
Competitive Conference papers (less than 10% acceptance rate):
(2) A. Painsky and S. Rosset,
"Compressing Random Forests",
IEEE 16th International Conference on Data Mining (ICDM), Dec 2016 [link]
(1) A. Painsky and S. Rosset,
"Exclusive Row Biclustering for Gene Expression Using a Combinatorial Auction Approach",
IEEE 12th International Conference on Data Mining (ICDM), Dec 2012 [link]
Conference papers:
(9) A. Painsky,
"A Data-Driven Missing Mass Estimation Framework",
IEEE International Symposium on Information Theory (ISIT), Jun 2022
(8) A. Painsky,
"Refined Convergence Rates of the Good-Turing Estimator",
IEEE Information Theory Workshop (ITW), Oct 2021[link]
(7) A. Painsky and G.W. Wornell,
"On the Universality of the Logistic Loss Function",
IEEE International Symposium on Information Theory (ISIT), May 2018 [link]
(6) A. Painsky, S. Rosset and M. Feder,
"Binary Independent Component Analysis: Theory, Bounds And Algorithms",
IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), Sep 2016 [link]
(5) A. Painsky, S. Rosset and M. Feder,
"A Simple and Efficient Approach for Adaptive Entropy Coding Over Large Alphabets",
Data Compression Conference (DCC), Apr 2016 [link]
(4) A. Painsky, S. Rosset and M.Feder,
"Universal Compression of Memoryless Sources over Large Alphabets via Independent Component Analysis",
Data Compression Conference (DCC), Apr 2015 [link]
(3) A. Painsky, S. Rosset and M. Feder,
"Generalized Binary Independent Component Analysis",
IEEE International Symposium on Information Theory (ISIT), Jul 2014 [link]
(2) A. Painsky, S. Rosset and M. Feder,
"Memoryless Representation of Markov Processes",
IEEE International Symposium on Information Theory (ISIT), Jul 2013 [link]
(1) A. Painsky,
"First Order Multiple Hypothesis Tracking for the Global Nearest Neighbor Correlation Approach",
IEEE Workshop on Sensor Data Fusion, Sep 2010. [link]
PhD thesis (Statistics):
Generalized Independent Component Analysis over Finite Alphabets, Tel Aviv University, 2016
Online version [link]
Matser thesis (Statistics):
Exclusive Row Biclustering for Gene Expression Using a Combinatorial Auction Approach, Tel Aviv University, 2011
Online version [link]
Book Chapters:
(1) A. Painsky,
"Quality Assessment and Evaluation Criteria in Supervised Learning",
The Handbook of Machine Learning for Data Science, Springer Publishing, Aug 2023 [link]
In bold - our group members