Technical Reports and Current Submissions:

(2)  S. Rosset, R. Heller, A. Painsky and E. Aharoni, 

      "Optimal Procedures for Multiple Testing Problems",

      Submitted to the Annals of Statistics, Under Review,  Nov 2018

      Online version - [link]

(1)  R. Shwartz-Ziv*, A. Painsky* and N. Tishby, 

      "Representation Compression and Generalization in Deep Neural Networks",

      Technical Report, Jul 2018

*Authors contributed equally

Journal Papers:

(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:

 

(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