714 citations · 1.1k across the 12 of their papers we have counts for
12 papers
Corrupted Sensing: Novel Guarantees for Separating Structured Signals
Rina Foygel, Lester Mackey
We study the problem of corrupted sensing, a generalization of compressed sensing in which one aims to recover a signal from a collection of corrupted or unreliable measurements. W…
Nonparametric Reduced Rank Regression
Rina Foygel, Michael Horrell, Mathias Drton +1
We propose an approach to multivariate nonparametric regression that generalizes reduced rank regression for linear models. An additive model is estimated for each dimension of a $…
Matrix reconstruction with the local max norm
Rina Foygel, Nathan Srebro, Ruslan Salakhutdinov
We introduce a new family of matrix norms, the "local max" norms, generalizing existing methods such as the max norm, the trace norm (nuclear norm), and the weighted or smoothed we…
Sparse Prediction with the -Support Norm
Andreas Argyriou, Rina Foygel, Nathan Srebro
We derive a novel norm that corresponds to the tightest convex relaxation of sparsity combined with an penalty. We show that this new {\em -support norm} provides a tig…
Bayesian model choice and information criteria in sparse generalized linear models
Rina Foygel, Mathias Drton
We consider Bayesian model selection in generalized linear models that are high-dimensional, with the number of covariates p being large relative to the sample size n, but sparse i…
Fast-rate and optimistic-rate error bounds for L1-regularized regression
Rina Foygel, Nathan Srebro
We consider the prediction error of linear regression with L1 regularization when the number of covariates p is large relative to the sample size n. When the model is k-sparse and…