18 citations · 32 across the 15 of their papers we have counts for
10 papers · 1 filter
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…