Technical Reports (Under Review):
(2) A. Painsky and A. Kontorovich
"Distribution Estimation under the Infinity Norm" [link]
(1) S. Anuk, T. Bendory and A. Painsky,
"Optimal detection of non-overlapping images via combinatorial auction" [link]
Journal Papers:
(24) Y. Nissenbaum and A. Painsky,
"Cross-validated Tree-based Models for Multi-target Learning",
Frontiers in Artificial Intelligence, Accepted Jan 2024. To Appear [link]
(23) A. Painsky,
"Confidence Intervals for Parameters of Unobserved Events",
Journal of the American Statistical Association (JASA), Accepted Jan 2024. To Appear [link]
(22) D. Marton and A. Painsky,
"Good-Bootstrap: Simultaneous Confidence Intervals for Large Alphabet Distributions",
Journal of Nonparametric Statistics, Accepted Jan 2024. To Appear [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 Guarentees 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, To Appear, Accepted 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