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Found 10 papers, 3 papers with code
Iteratively Reweighted $\ell_1$-Penalized Robust Regression
This paper investigates tradeoffs among optimization errors, statistical rates of convergence and the effect of heavy-tailed errors for high-dimensional robust regression with nonconvex regularization.
User-Friendly Covariance Estimation for Heavy-Tailed Distributions
We offer a survey of recent results on covariance estimation for heavy-tailed distributions.
Methodology Statistics Theory Statistics Theory
Matrix Completion via Max-Norm Constrained Optimization
Matrix completion has been well studied under the uniform sampling model and the trace-norm regularized methods perform well both theoretically and numerically in such a setting.
Max-Norm Optimization for Robust Matrix Recovery
This paper studies the matrix completion problem under arbitrary sampling schemes.
A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion
The rate of convergence for the estimate is obtained.
Smoothed Quantile Regression with Large-Scale Inference
Our numerical studies confirm the conquer estimator as a practical and reliable approach to large-scale inference for quantile regression.
Statistics Theory Methodology Statistics Theory
Communication-Constrained Distributed Quantile Regression with Optimal Statistical Guarantees
We address the problem of how to achieve optimal inference in distributed quantile regression without stringent scaling conditions.
How do noise tails impact on deep ReLU networks?
This paper investigates the stability of deep ReLU neural networks for nonparametric regression under the assumption that the noise has only a finite p-th moment.
Retire: Robust Expectile Regression in High Dimensions
In this paper, we propose and study (penalized) robust expectile regression (retire), with a focus on iteratively reweighted $\ell_1$-penalization which reduces the estimation bias from $\ell_1$-penalization and leads to oracle properties.
Private Optimal Inventory Policy Learning for Feature-based Newsvendor with Unknown Demand
By leveraging the structure of the newsvendor problem, we attain a faster excess population risk bound compared to that obtained from an indiscriminate application of existing results for general nonsmooth convex loss.
