Technical Reports:

(3)  U. Shiterburd, T, Bendory and A. Painsky,

      "K-sample Multiple Hypothesis Testing for Signal Detection",

      Submitted to the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),

      Under Review,  Sep 2022

      Online version - [link]

(2)  M. Roth, A. Painsky and T. Bendory, 

      "Signal Detection with Dynamic Programming",

      Submitted to the IEEE Transactions on Signal Processing, Under Review,  Sep 2022

      Online version - [link]

(1)  D. Marton and A. Painsky

      "Good-Bootstrap: Simultaneous Confidence Intervals for Large Alphabet Distributions",

      Submitted to Statistics and Computing, Under Review,  Sep 2022

      Online version - [link]

Journal Papers:

(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), To Appear, Accepted Oct 2022  

(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. To Appear Mar 2020

                                               In bold - our group members