arXiv research

Huber regression assessed for robustness in statistical learning.

Improved robust regression with clean covariates achieves better rates than Huber's model.

This paper examines how noise affects deep neural networks and improves their performance.

Paper supports robust estimation in regression with heavy-tailed errors.

Paper proposes an algorithm for robust estimation using Huber's criterion.

We propose an algorithm, semismooth Newton coordinate descent (SNCD), for the elastic-net penalized Huber loss regression and quantile regression in high dimensional settings. Unlike existing coordinate descent type algorithms, the SNCD updates each regression coefficient and its corresponding subgradient simultaneousl…

We present a new method for high-dimensional linear regression when a scale parameter of the additive errors is unknown. The proposed estimator is based on a penalized Huber $M$-estimator, for which theoretical results on estimation error have recently been proposed in high-dimensional statistics literature. However, t…

Deep Huber QRNs predict Huber quantiles for house prices.

New algorithms robust to adversarial data achieve optimal performance.

The paper introduces a new FOR framework using Huber and ε-insensitive losses.

Two algorithms tackle heavy-tailed rewards in reinforcement learning with linear function approximation.

New algorithm recovers sparse signals robustly against Gaussian noise and adaptive adversaries.

New algorithm reduces heavy-tailed linear bandits' computational cost.

Paper addresses covariate shift in deep learning regression models.

Study improves error bounds for sparse regression with heavy-tailed covariates.

Robustly estimates linear regression coefficients with adversarial and noisy data.

Improved robust regression for heavy-tailed and contaminated data.

Near-optimal algorithms for mean estimation and linear regression with Gaussian covariates and Huber contamination.

We solve robust regression and matrix completion problems with sparse and low-rank models.

Study improves robust nonparametric regression in heavy-tailed noise.

The Huber loss is a robust loss function used for a wide range of regression tasks. To utilize the Huber loss, a parameter that controls the transitions from a quadratic function to an absolute value function needs to be selected. We believe the standard probabilistic interpretation that relates the Huber loss to the H…

A robust Gaussian process model using Huber likelihood for outlier resistance.

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. When the additive errors in linear models have only bounded second moment, we show that iteratively reweighted $\ell_1…

Study improves $H$-consistency bounds for regression analysis.

Robust estimators for Gaussian sparse tasks with optimal error under contamination.

This paper studies robust regression in the settings of Huber's $ε$-contamination models. We consider estimators that are maximizers of multivariate regression depth functions. These estimators are shown to achieve minimax rates in the settings of $ε$-contamination models for various regression problems including nonpa…

Paper proposes robust geodesic regression for manifold data.

We consider new formulations and methods for sparse quantile regression in the high-dimensional setting. Quantile regression plays an important role in many applications, including outlier-robust exploratory analysis in gene selection. In addition, the sparsity consideration in quantile regression enables the explorati…

In this paper, we generalize Huber's criterion to multichannel sparse recovery problem of complex-valued measurements where the objective is to find good recovery of jointly sparse unknown signal vectors from the given multiple measurement vectors which are different linear combinations of the same known elementary vec…

ABRF uses attention weights to improve RF performance.

AGBoost uses attention weights to improve GBM for regression problems.

We consider the non-parametric regression problem under Huber's $ε$-contamination model, in which an $ε$ fraction of observations are subject to arbitrary adversarial noise. We first show that a simple local binning median step can effectively remove the adversary noise and this median estimator is minimax optimal up t…

New robust regression method works with fewer data points than previous methods.

Random forests with attention and self-attention improve regression performance.

AdapDISCOM tackles high-dimensional multimodal data with missingness and errors, improving prediction and biomarker selection.

Study robust linear regression with outliers, providing exact asymptotics for ERM performance.

New robust method for high-dimensional data analysis in imaging studies.

Generalized Huber's theorem for specific manifold curvature types.

A new framework for time series forecasting that adapts to varying patterns.

ABIForest improves anomaly detection using attention weights.

Heavy Lasso improves robustness in high-dimensional linear regression with heavy-tailed errors.

The paper proves new inequalities on the unit ball in higher dimensions.

Unified approach for robust low rank matrix estimation with adversaries.

R2T hybrid model improves robust regression for asymmetric noise.

Proposes a new Huber loss combining absolute and quadratic properties.

Smooth DNNs mitigate the curse of dimensionality in uniform convergence for various regression tasks.

Paper introduces a new robust loss function for RL.

Unified framework for fair regression under demographic parity.