42 citations · 81 across the 21 of their papers we have counts for
7 papers · 1 filter
Linear Regression from 1-bit Quantized Data
Daniel Hill, Martin Slawski
Motivated by the prevalence of environments in which data is abundant while resources for storage and/or transmission might be scarce, we study linear regression when predictors, t…
Identifiability in Unlinked Linear Regression: Some Results and Open Problems
Fadoua Balabdaoui, Martin Slawski, Jonathan Steffani
A tacit assumption in classical linear regression problems is the full knowledge of the existing link between the covariates and responses. In Unlinked Linear Regression (ULR) this…
Permuted and Unlinked Monotone Regression in : an approach based on mixture modeling and optimal transport
Martin Slawski, Bodhisattva Sen
Suppose that we have a regression problem with response variable Y in and predictor X in , for . In permuted or unlinked regression we have a…
Linear Regression with Sparsely Permuted Data
Martin Slawski, Emanuel Ben-David
In regression analysis of multivariate data, it is tacitly assumed that response and predictor variables in each observed response-predictor pair correspond to the same entity or u…
On Principal Components Regression, Random Projections, and Column Subsampling
Martin Slawski
Principal Components Regression (PCR) is a traditional tool for dimension reduction in linear regression that has been both criticized and defended. One concern about PCR is that o…
Estimation of positive definite M-matrices and structure learning for attractive Gaussian Markov Random fields
Martin Slawski, Matthias Hein
Consider a random vector with finite second moments. If its precision matrix is an M-matrix, then all partial correlations are non-negative. If that random vector is additionally G…