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(9)   A. Painsky,

      "Confidence Intervals for Parameters of  Unobserved Events",

       Journal of the American Statistical Association (JASA), Accepted Jan 2024. To Appear [link]

(8)  A. Painsky

      "Large Alphabet Inference",

        Information and Inference. Vol. 12, Issue 4, pp. 3067–3086, Dec 2023 [link]

(7) A. Painsky

      "Convergence Guarentees for the Good-Turing Estimator",

       Journal of  Machine Learning Research (JMLR), Vol 23, Issue 27, Sep 2022 [link 

(6) A. Painsky

      "Generalized Good-Turing Improves Missing Mass Estimation",

       Journal of the American Statistical Association (JASA), Jan 2022  [link]

(5)  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] 

(4)  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]

(3)  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]

(2)  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]

(1)  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]

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