​
(9) A. Painsky,
"Confidence Intervals for Parameters of Unobserved Events",
Journal of the American Statistical Association (JASA), Accepted Jan 2024. To Appear [link]
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(8) A. Painsky,
Information and Inference. Vol. 12, Issue 4, pp. 3067–3086, Dec 2023 [link]
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(7) A. Painsky,
"Convergence Guarentees for the Good-Turing Estimator",
Journal of Machine Learning Research (JMLR), Vol 23, Issue 27, Sep 2022 [link]
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(6) A. Painsky,
"Generalized Good-Turing Improves Missing Mass Estimation",
Journal of the American Statistical Association (JASA), Jan 2022 [link]
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(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]
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(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]
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(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]
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(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]
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(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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