18 citations · 32 across the 15 of their papers we have counts for
18 papers
Confidence Intervals for Unobserved Events
Amichai Painsky
Consider a finite sample from an unknown distribution over a countable alphabet. Unobserved events are alphabet symbols which do not appear in the sample. Estimating the probabilit…
K-sample Multiple Hypothesis Testing for Signal Detection
Uriel Shiterburd, Tamir Bendory, Amichai Painsky
This paper studies the classical problem of estimating the locations of signal occurrences in a noisy measurement. Based on a multiple hypothesis testing scheme, we design a K-samp…
Detecting non-overlapping signals with dynamic programming
Mordechai Roth, Amichai Painsky, Tamir Bendory
This paper studies the classical problem of detecting the locations of signal occurrences in a one-dimensional noisy measurement. Assuming the signal occurrences do not overlap, we…
Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection
Afek Ilay Adler, Amichai Painsky
Gradient Boosting Machines (GBM) are among the go-to algorithms on tabular data, which produce state of the art results in many prediction tasks. Despite its popularity, the GBM fr…
Neural Joint Entropy Estimation
Yuval Shalev, Amichai Painsky, Irad Ben-Gal
Estimating the entropy of a discrete random variable is a fundamental problem in information theory and related fields. This problem has many applications in various domains, inclu…
Innovation Representation of Stochastic Processes with Application to Causal Inference
Amichai Painsky, Saharon Rosset, Meir Feder
Typically, real-world stochastic processes are not easy to analyze. In this work we study the representation of any stochastic process as a memoryless innovation process triggering…
Lossless (and Lossy) Compression of Random Forests
Amichai Painsky, Saharon Rosset
Ensemble methods are among the state-of-the-art predictive modeling approaches. Applied to modern big data, these methods often require a large number of sub-learners, where the co…
Non-linear Canonical Correlation Analysis: A Compressed Representation Approach
Amichai Painsky, Meir Feder, Naftali Tishby
Canonical Correlation Analysis (CCA) is a linear representation learning method that seeks maximally correlated variables in multi-view data. Non-linear CCA extends this notion to…
Bregman Divergence Bounds and Universality Properties of the Logarithmic Loss
Amichai Painsky, Gregory W. Wornell
A loss function measures the discrepancy between the true values and their estimated fits, for a given instance of data. In classification problems, a loss function is said to be p…
MSc Dissertation: Exclusive Row Biclustering for Gene Expression Using a Combinatorial Auction Approach
Amichai Painsky
The availability of large microarray data has led to a growing interest in biclustering methods in the past decade. Several algorithms have been proposed to identify subsets of gen…
Linear Independent Component Analysis over Finite Fields: Algorithms and Bounds
Amichai Painsky, Saharon Rosset, Meir Feder
Independent Component Analysis (ICA) is a statistical tool that decomposes an observed random vector into components that are as statistically independent as possible. ICA over fin…
PhD Dissertation: Generalized Independent Components Analysis Over Finite Alphabets
Amichai Painsky
Independent component analysis (ICA) is a statistical method for transforming an observable multi-dimensional random vector into components that are as statistically independent as…
Outperforming Good-Turing: Preliminary Report
Amichai Painsky, Meir Feder
Estimating a large alphabet probability distribution from a limited number of samples is a fundamental problem in machine learning and statistics. A variety of estimation schemes h…
On the Universality of the Logistic Loss Function
Amichai Painsky, Gregory W. Wornell
A loss function measures the discrepancy between the true values (observations) and their estimated fits, for a given instance of data. A loss function is said to be proper (unbias…
Optimal and Maximin Procedures for Multiple Testing Problems
Saharon Rosset, Ruth Heller, Amichai Painsky +1
Multiple testing problems are a staple of modern statistical analysis. The fundamental objective of multiple testing procedures is to reject as many false null hypotheses as possib…
Gaussian Lower Bound for the Information Bottleneck Limit
Amichai Painsky, Naftali Tishby
The Information Bottleneck (IB) is a conceptual method for extracting the most compact, yet informative, representation of a set of variables, with respect to the target. It genera…
Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance
Amichai Painsky, Saharon Rosset
Recursive partitioning approaches producing tree-like models are a long standing staple of predictive modeling, in the last decade mostly as ``sub-learners'' within state of the ar…
Generalized Independent Component Analysis Over Finite Alphabets
Amichai Painsky, Saharon Rosset, Meir Feder
Independent component analysis (ICA) is a statistical method for transforming an observable multidimensional random vector into components that are as statistically independent as…