Search Results for author:
Found 5 papers, 1 papers with code
A spectral least-squares-type method for heavy-tailed corrupted regression with unknown covariance \& heterogeneous noise
We wish to estimate a $p$-dimensional parameter $b^*$ given such sample of a label-feature pair $(y, x)$ satisfying $y=\langle x, b^*\rangle+\xi$ with heavy-tailed $(x,\xi)$.
Outlier-robust sparse/low-rank least-squares regression and robust matrix completion
For these problems, we show novel near-optimal "subgaussian" estimation rates of the form $r(n, d_{e})+\sqrt{\log(1/\delta)/n}+\epsilon\log(1/\epsilon)$, valid with probability at least $1-\delta$.
Outlier-robust estimation of a sparse linear model using \ell_1-penalized Huber's M-estimator
We study the problem of estimating a $p$-dimensional $s$-sparse vector in a linear model with Gaussian design.
Outlier-robust estimation of a sparse linear model using $\ell_1$-penalized Huber's $M$-estimator
We study the problem of estimating a $p$-dimensional $s$-sparse vector in a linear model with Gaussian design and additive noise.
Restricted eigenvalue property for corrupted Gaussian designs
Motivated by the construction of tractable robust estimators via convex relaxations, we present conditions on the sample size which guarantee an augmented notion of Restricted Eigenvalue-type condition for Gaussian designs.
Statistics Theory Statistics Theory
