arXiv research

ORAT improves model robustness against outliers and adversarial attacks.

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

A CAE improves DNN's outlier and adversary defense.

Study improves robustness and sparsity in linear regression with adversarial outliers and heavy-tailed noise.

We study the problem of robust subspace recovery (RSR) in the presence of adversarial outliers. That is, we seek a subspace that contains a large portion of a dataset when some fraction of the data points are arbitrarily corrupted. We first examine a theoretical estimator that is intractable to calculate and use it to …

Paper proposes methods to make OT robust to outliers.

The paper explores transferability of adversarial examples between convex and 01 loss models, finding non-transferability due to different decision boundaries caused by outliers.

Deep neural networks are vulnerable to adversarial examples. Prior defenses attempted to make deep networks more robust by either changing the network architecture or augmenting the training set with adversarial examples, but both have inherent limitations. Motivated by recent research that shows outliers in the traini…

Paper proposes a new black-box adversarial attack for clustering models.

New method robustifies topological data analysis against outliers.

ATOM improves robust OOD detection by mining informative auxiliary examples.

New algorithm recovers mixture means even with many outliers.

Outlier detection is an important topic in machine learning and has been used in a wide range of applications. In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution. However, due to the sparsity of data in high-dimensional spac…

For the degree corrected stochastic block model in the presence of arbitrary or even adversarial outliers, we develop a convex-optimization-based clustering algorithm that includes a penalization term depending on the positive deviation of a node from the expected number of edges to other inliers. We prove that under m…

New algorithm robust to outliers in Bayesian Optimization.

Paper solves outlier robust mean estimation near breakdown point.

Develops metrics to identify outliers in machine learning datasets.

Paper tackles outlier detection in signals modeled by generative models with theoretical guarantees.

We develop efficient algorithms for estimating low-degree moments of unknown distributions in the presence of adversarial outliers. The guarantees of our algorithms improve in many cases significantly over the best previous ones, obtained in recent works of Diakonikolas et al, Lai et al, and Charikar et al. We also sho…

The paper provides robustness guarantees for mode estimation in bandits.

This paper considers the problem of recovering signals from compressed measurements contaminated with sparse outliers, which has arisen in many applications. In this paper, we propose a generative model neural network approach for reconstructing the ground truth signals under sparse outliers. We propose an iterative al…

Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis. Autoencoder neural networks learn to reconstruct normal images, and hence can classify those images as anomalies, where the reconstruction error exceeds som…

We study two problems in high-dimensional robust statistics: \emph{robust mean estimation} and \emph{outlier detection}. In robust mean estimation the goal is to estimate the mean $μ$ of a distribution on $\mathbb{R}^d$ given $n$ independent samples, an $\varepsilon$-fraction of which have been corrupted by a malicious…

Robustly estimates sparse data with corrupted outliers.

Develops a robust GMM estimator for outlier-tolerant inference.

Robustly learns Ising models with corrupted data.

Paper introduces robust, dynamic coreset for outlier-resilient learning problems.

In this paper, we investigate the adversarial robustness of multivariate $M$-Estimators. In the considered model, after observing the whole dataset, an adversary can modify all data points with the goal of maximizing inference errors. We use adversarial influence function (AIF) to measure the asymptotic rate at which t…

Gradient descent solves robust mean estimation in high dimensions.

The paper analyzes how conformal prediction works with contaminated reference data.

SONAR improves outlier detection for streaming data with strong theoretical guarantees.

New framework robustly handles outliers in Wasserstein DRO for better decision-making.

This work robustifies Wasserstein distance estimation with MoM estimators for outlier-polluted data.

Online TERM improves robustness and fairness in streaming data.

A new robust regression method handles outliers in high-dimensional data.

Nowadays more and more data are gathered for detecting and preventing cyber attacks. In cyber security applications, data analytics techniques have to deal with active adversaries that try to deceive the data analytics models and avoid being detected. The existence of such adversarial behavior motivates the development…

Christoffel function characterizes the corruption a bounded-degree certificate cannot remove in robust halfspace learning.

Machine Learning (ML) models are applied in a variety of tasks such as network intrusion detection or Malware classification. Yet, these models are vulnerable to a class of malicious inputs known as adversarial examples. These are slightly perturbed inputs that are classified incorrectly by the ML model. The mitigation…

Community detection, which aims to cluster $N$ nodes in a given graph into $r$ distinct groups based on the observed undirected edges, is an important problem in network data analysis. In this paper, the popular stochastic block model (SBM) is extended to the generalized stochastic block model (GSBM) that allows for ad…

01 loss is robust to outliers and adversarial attacks, outperforming convex losses.

Efficiently estimates Gaussian distributions privately and robustly.

In this paper, we study the adversarial robustness of subspace learning problems. Different from the assumptions made in existing work on robust subspace learning where data samples are contaminated by gross sparse outliers or small dense noises, we consider a more powerful adversary who can first observe the data matr…

FCI method uses flow-based techniques to improve prediction confidence.

New algorithm estimates eigenspace with faulty nodes, matching performance of existing methods.

Class-conditional generative models hold promise to overcome the shortcomings of their discriminative counterparts. They are a natural choice to solve discriminative tasks in a robust manner as they jointly optimize for predictive performance and accurate modeling of the input distribution. In this work, we investigate…

New algorithm resists contamination in high-dimensional regression with optimal performance.

New algorithm for robust regression with subgaussian error bound.

We present a Distributionally Robust Optimization (DRO) approach to estimate a robustified regression plane in a linear regression setting, when the observed samples are potentially contaminated with adversarially corrupted outliers. Our approach mitigates the impact of outliers through hedging against a family of dist…