arXiv research

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

New proof shows faster convergence rate for robust estimation with Lasso in adversarially contaminated outputs.

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

Improved robust regression for heavy-tailed and contaminated data.

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

Study minimax rates for density estimation under Huber contamination and Besov IPM losses.

A robust loss for anomaly mitigation and unsupervised contamination classification

In this paper, we develop connections between two seemingly disparate, but central, models in robust statistics: Huber's epsilon-contamination model and the heavy-tailed noise model. We provide conditions under which this connection provides near-statistically-optimal estimators. Building on this connection, we provide…

Study sample complexity of robust binary hypothesis testing under different contamination models.

Study on private and robust multi-armed bandits with contaminated heavy-tailed rewards.

Study minimax robustness in statistical estimation under Wasserstein contamination.

New algorithms improve robust estimation in contaminated Gaussian models.

New algorithms robust to adversarial data achieve optimal performance.

Paper presents robust confidence sequences for means with known moment bounds and arbitrary corruption.

ABRF uses attention weights to improve RF performance.

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…

A new robust Wasserstein distance is proposed to handle outliers in probability distributions.

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

Robustly estimates linear regression coefficients with adversarial and noisy data.

Efficient streaming algorithms for robust statistics with near-optimal memory.

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…

SHIFT improves robustness in estimating dose-response functions with heavy-tailed contamination.

ABIForest improves anomaly detection using attention weights.

AGBoost uses attention weights to improve GBM for regression problems.

Bayesian X-Learner calibrates uncertainty and robustness for CATE estimation under heavy-tailed data.

New method uses robust estimators for Newton's method in empirical risk minimization.

Study enhances robustness of In-CVaR based regression models under perturbation and contamination.

We study Empirical Risk Minimizers (ERM) and Regularized Empirical Risk Minimizers (RERM) for regression problems with convex and $L$-Lipschitz loss functions. We consider a setting where $|\cO|$ malicious outliers contaminate the labels. In that case, under a local Bernstein condition, we show that the $L_2$-error rat…

Efficiently estimates mean in contaminated Gaussian data with near-optimal sample complexity.

LARF improves random forests with attention mechanisms and contamination models.

Robust covariance testing requires significantly more samples in contaminated data.

New method calibrates ambiguity sets for robust decision-making under contamination.

Replacing MSE with f-divergence in diffusion models improves robustness under data contamination.

Unified approach for robust low rank matrix estimation with adversaries.

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

Paper tackles robust matrix completion with heavy-tailed noise.

We propose a novel exponentially-modified Gaussian (EMG) mixture residual model. The EMG mixture is well suited to model residuals that are contaminated by a distribution with positive support. This is in contrast to commonly used robust residual models, like the Huber loss or $\ell_1$, which assume a symmetric contami…

Heavy-tailed outliers are more resilient to robust estimation than adversarial ones.

We provide a new computationally-efficient class of estimators for risk minimization. We show that these estimators are robust for general statistical models: in the classical Huber epsilon-contamination model and in heavy-tailed settings. Our workhorse is a novel robust variant of gradient descent, and we provide cond…

The paper develops adaptive confidence intervals for Efron's Gaussian two-groups model with unknown contamination.

New method recovers sparse signals from nonlinear observations with robust error bounds.

Study examines robust regression in high dimensions with heavy-tailed data.

Estimating average causal effect (ACE) is useful whenever we want to know the effect of an intervention on a given outcome. In the absence of a randomized experiment, many methods such as stratification and inverse propensity weighting have been proposed to estimate ACE. However, it is hard to know which method is opti…

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

Generalized Huber's theorem for specific manifold curvature types.

Huber regression assessed for robustness in statistical learning.

Robust scatter estimation is a fundamental task in statistics. The recent discovery on the connection between robust estimation and generative adversarial nets (GANs) by Gao et al. (2018) suggests that it is possible to compute depth-like robust estimators using similar techniques that optimize GANs. In this paper, we …

Robust estimation under Huber's $ε$-contamination model has become an important topic in statistics and theoretical computer science. Statistically optimal procedures such as Tukey's median and other estimators based on depth functions are impractical because of their computational intractability. In this paper, we est…