arXiv research

A new Heckman selection model uses a bivariate contaminated normal distribution for more accurate data analysis.

Robust score matching improves parameter estimation in contaminated data.

Robust GQDA improves classification accuracy in non-Normal data.

Improves anomaly detection with contaminated unlabeled data.

Paper shows MoM is optimal under adversarial contamination for certain distributions.

Many machine learning problems can be characterized by mutual contamination models. In these problems, one observes several random samples from different convex combinations of a set of unknown base distributions. It is of interest to decontaminate mutual contamination models, i.e., to recover the base distributions ei…

New algorithm reduces contamination in supervised learning.

This paper studies active learning in the context of robust statistics. Specifically, we propose a variant of the Best Arm Identification problem for \emph{contaminated bandits}, where each arm pull has probability $\varepsilon$ of generating a sample from an arbitrary contamination distribution instead of the true und…

Bayesian method estimates contamination factor for unsupervised anomaly detection.

Paper uses DNN-based MoM-GAN to estimate contaminated data distributions.

A new method optimizes anomaly scoring from score distribution to improve AD performance.

Efficiently estimates sparse linear regression with heavy-tailed and outlier-contaminated data.

New test ensures quality of shared data in machine learning.

New method trims network data to resist adversarial contamination.

ConvMMD improves inference in noisy data.

Algorithm identifies Pareto set in bandits with contaminated feedback.

Paper proposes robust estimators for GANs under Wasserstein contamination.

A low rank matrix X has been contaminated by uniformly distributed noise, missing values, outliers and corrupt entries. Reconstruction of X from the singular values and singular vectors of the contaminated matrix Y is a key problem in machine learning, computer vision and data science. In this paper we show that common…

Robust covariance testing requires significantly more samples in contaminated data.

Motivated by applications of bandit algorithms in education, we consider a stochastic multi-armed bandit problem with $\varepsilon$-contaminated rewards. We allow an adversary to give arbitrary unbounded contaminated rewards with full knowledge of the past and future. We impose the constraint that for each time $t$ the…

Robustly aligns datasets with partial GW distance to handle contamination.

Study improves robustness of Bayesian inference for cognitive models.

Neural networks compress and sample WDN contamination dynamics efficiently.

Mixtures of multivariate contaminated shifted asymmetric Laplace distributions are developed for handling asymmetric clusters in the presence of outliers (also referred to as bad points herein). In addition to the parameters of the related non-contaminated mixture, for each (asymmetric) cluster, our model has one param…

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

Theoretical study on AI models' resilience to data contamination during recursive training.

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

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

Theoretical study shows AI models can recover from contaminated training data.

The paper shows how training with synthetic data can lead to model improvement, not degradation, under certain conditions.

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

Study robustness of split conformal prediction in data contamination setting.

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

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

Study shows efficient algorithms for noiseless linear regression require quadratic sample complexity in contamination rate.

A new method detects distribution shifts faster than existing CTMs.

Many machine learning problems can be characterized by mutual contamination models. In these problems, one observes several random samples from different convex combinations of a set of unknown base distributions and the goal is to infer these base distributions. This paper considers the general setting where the base …

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

New model detects anomalies without needing clean data.

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

Study improves MMD estimation for two distributions with mismeasured data.

Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup). We develop a new technique for removing this contamination using machine learning and convolutional neural networks. The network takes as input the ene…

New algorithm optimizes robust estimation under mixed local and global corruptions.

This work is motivated by the problem of image mis-registration in remote sensing and we are interested in determining the resulting loss in the accuracy of pattern classification. A statistical formulation is given where we propose to use data contamination to model and understand the phenomenon of image mis-registrat…

Researchers develop a new spatial process model for non-Gaussian data.

New method estimates discrete distributions while protecting privacy.

New algorithms robust to adversarial data achieve optimal performance.

A contamination in a 3-manifold is an object interpolating between the contact structure and the lamination. Contaminations seem to provide a link between 3-dimensional contact geometry and the classical topology of 3-manifolds, as described in a separate paper. In this paper we deal with contaminations carried by bran…