First Order Multiple Hypothesis Testing for the Global Nearest Neighbor Data Correlation Approach. - Amichai Painsky (GI Jahrestagung (2), 2010)

Exclusive Row Biclustering for Gene Expression Using a Combinatorial Auction Approach. - Amichai Painsky; Saharon Rosset (ICDM, 2012)

Memoryless representation of Markov processes. - Amichai Painsky; Saharon Rosset; Meir Feder (ISIT, 2013)

Generalized binary independent component analysis. - Amichai Painsky; Saharon Rosset; Meir Feder (ISIT, 2014)

Optimal Set Cover Formulation for Exclusive Row Biclustering of Gene Expression. - Amichai Painsky; Saharon Rosset (J. Comput. Sci. Technol., 2014)

Universal Compression of Memoryless Sources over Large Alphabets via Independent Component Analysis. - Amichai Painsky; Saharon Rosset; Meir Feder (DCC, 2015)

A Simple and Efficient Approach for Adaptive Entropy Coding over Large Alphabets. - Amichai Painsky; Saharon Rosset; Meir Feder (DCC, 2016)

Compressing Random Forests. - Amichai Painsky; Saharon Rosset (ICDM, 2016)

Binary independent component analysis: Theory, bounds and algorithms. - Amichai Painsky; Saharon Rosset; Meir Feder (MLSP, 2016)

Isotonic Modeling with Non-Differentiable Loss Functions with Application to Lasso Regularization. - Amichai Painsky; Saharon Rosset (IEEE Trans. Pattern Anal. Mach. Intell., 2016)

Generalized Independent Component Analysis Over Finite Alphabets. - Amichai Painsky; Saharon Rosset; Meir Feder (IEEE Trans. Inf. Theory, 2016)

Gaussian Lower Bound for the Information Bottleneck Limit. - Amichai Painsky; Naftali Tishby (J. Mach. Learn. Res., 2017)

Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance. - Amichai Painsky; Saharon Rosset (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

Large Alphabet Source Coding Using Independent Component Analysis. - Amichai Painsky; Saharon Rosset; Meir Feder (IEEE Trans. Inf. Theory, 2017)

On the Universality of the Logistic Loss Function. - Amichai Painsky; Gregory W. Wornell (ISIT, 2018)

Generalized Independent Components Analysis Over Finite Alphabets. - Amichai Painsky (CoRR, 2018)

MSc Dissertation: Exclusive Row Biclustering for Gene Expression Using a Combinatorial Auction Approach. - Amichai Painsky (CoRR, 2018)

Bregman Divergence Bounds and the Universality of the Logarithmic Loss. - Amichai Painsky; Gregory W. Wornell (CoRR, 2018)

Lossless (and Lossy) Compression of Random Forests. - Amichai Painsky; Saharon Rosset (CoRR, 2018)

An Information-Theoretic Framework for Non-linear Canonical Correlation Analysis. - Amichai Painsky; Meir Feder; Naftali Tishby (CoRR, 2018)

Linear Independent Component Analysis Over Finite Fields: Algorithms and Bounds. - Amichai Painsky; Saharon Rosset; Meir Feder (IEEE Trans. Signal Process., 2018)

Lossless Compression of Random Forests. - Amichai Painsky; Saharon Rosset (J. Comput. Sci. Technol., 2019)

Nonlinear Canonical Correlation Analysis: A Compressed Representation Approach. - Amichai Painsky; Meir Feder; Naftali Tishby (Entropy, 2020)

Innovation Representation of Stochastic Processes With Application to Causal Inference. - Amichai Painsky; Saharon Rosset; Meir Feder (IEEE Trans. Inf. Theory, 2020)

Bregman Divergence Bounds and Universality Properties of the Logarithmic Loss. - Amichai Painsky; Gregory W. Wornell (IEEE Trans. Inf. Theory, 2020)

Refined Convergence Rates of the Good-Turing Estimator. - Amichai Painsky (ITW, 2021)

Robust Universal Inference. - Amichai Painsky; Meir Feder (Entropy, 2021)

A Data-driven Missing Mass Estimation Framework. - Amichai Painsky (ISIT, 2022)

Confidence Intervals for Unobserved Events. - Amichai Painsky (CoRR, 2022)

Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection. - Afek Ilay Adler; Amichai Painsky (Entropy, 2022)

Convergence Guarantees for the Good-Turing Estimator. - Amichai Painsky (J. Mach. Learn. Res., 2022)

Detecting Non-Overlapping Signals with Dynamic Programming. - Mordechai Roth; Amichai Painsky; Tamir Bendory (Entropy, 2023)

Distribution Estimation under the Infinity Norm. - Aryeh Kontorovich; Amichai Painsky (CoRR, 2024)

Cross-validated tree-based models for multi-target learning. - Yehuda Nissenbaum; Amichai Painsky (Frontiers Artif. Intell., 2024)

Neural Joint Entropy Estimation. - Yuval Shalev; Amichai Painsky; Irad Ben-Gal (IEEE Trans. Neural Networks Learn. Syst., 2024)

A Comparative Analysis of Discrete Entropy Estimators for Large-Alphabet Problems. - Assaf Pinchas; Irad Ben-Gal; Amichai Painsky (Entropy, 2024)

Near Optimal Inference for the Best-Performing Algorithm. - Amichai Painsky (CoRR, 2025)