A new algorithm identifies outliers in Gaussian clustering models.
problem Handling outliers in Gaussian model-based clustering.
method OCLUST algorithm removes least plausible points based on subset log-likelihoods until they adhere to a reference distribution.
result OCLUST inherently estimates the number of outliers.
New framework detects outliers in non-IID categorical data.
problem Existing outlier detection methods fail in non-IID data.
method Value-value graph-based representation and outlierness propagation.
result Significant improvement in AUC on complex data sets.
ECOD detects outliers without parameters, fast and simple.
problem Detecting outliers in large, high-dimensional datasets efficiently and interpretably.
method ECOD estimates empirical cumulative distribution functions per dimension, then computes tail probabilities and outlier scores.
result ECOD outperforms state-of-the-art methods in accuracy, efficiency, and scalability.
A theory of exceptional extreme events, characterized by their abnormal sizes compared with the rest of the distribution, is presented. Such outliers, called "dragon-kings", have been reported in the distribution of financial drawdowns, city-size distributions (e.g., Paris in France and London in the UK), in material f…
This paper proposes a method to improve neural network quantization without retraining.
problem Handling outliers in quantized DNN weights and activations.
method Outlier Channel Splitting (OCS) which duplicates channels containing outliers and halves their values.
result OCS outperforms state-of-the-art clipping techniques with minimal overhead.
Efficiently estimates sparse linear regression with heavy-tailed and outlier-contaminated data.
problem Estimating sparse linear regression coefficients with heavy-tailed and outlier-contaminated data.
method Efficient computation of estimators with sharp error bounds.
result Sharp error bounds for efficient estimators.
New method corrects deep neural network variance for outlier data.
problem Outliers in deep neural network outputs.
method Gradient Conjugate Priors (GCP) network.
result Corrected variance is in a small neighborhood of ground truth variance.
Transforms distance-based outlier scores into interpretable probabilistic estimates.
problem Difficult interpretation of distance-based outlier scores.
method Generic transformation of scores into probabilistic estimates using distance probability distributions.
result Probabilistic transformation improves interpretability without impacting detection performance.
New method clusters matrix-variate data with outliers.
problem Clustering matrix-variate data with outliers.
method Iterative approach using subset log-likelihoods.
result Extends OCLUST algorithm to matrix-variate normal data.
This work bridges outlier and drift detection by comparing inputs to a part of the reference distribution.
problem Monitoring machine learning models to ensure they operate within their validated distribution.
method Comparing a set of inputs to a chosen part of the reference distribution.
result A new approach that bridges outlier detection and drift detection.
HLoOP detects outliers in hyperbolic 2-space.
problem Detecting local outliers in hyperbolic 2-space.
method Combines nearest neighbor finding and probabilistic scoring in hyperbolic space.
result Promising results on WordNet dataset.
Failure of the main argument for the use of heavy tailed distribution in Finance is given. More precisely, one cannot observe so many outliers for Cauchy or for symmetric stable distributions as we have in reality. keywords:outliers; financial indexes; heavy tails; Cauchy distribution; stable distributions
Enhances predictive models against misspecification and outliers.
problem Suboptimal generalization under misspecification and outliers.
method Combines PACm ensemble bounds with a generalized logarithm score function. result Produces predictive distributions resistant to both misspecification and outliers.
A new method detects and corrects outliers using optimal transport.
problem Outliers in data can skew estimation results, leading to inaccurate conclusions.
method Optimal transport with a concave cost function for outlier detection and correction.
result The method effectively identifies and corrects outliers, improving estimation accuracy.
A new meta-analysis model detects and accommodates outliers.
problem Outliers in meta-analysis studies can skew results.
method Proposes a novel tMeta model using the t distribution for robustness. result Demonstrates superior performance in detecting and accommodating outliers.
New framework detects out-of-distribution data by considering intrinsic ID attributes in outliers.
problem Deploying reliable machine learning systems requires effective out-of-distribution detection.
method Structured multi-view-based out-of-distribution detection learning (MVOL) framework.
result MVOL effectively utilizes both auxiliary OOD datasets and wild datasets with noisy in-distribution data.
Improves data normality with robust transformations.
problem Skewed data distribution.
method Modified Box-Cox and Yeo-Johnson transformations with robust parameter estimation.
result Transformed data approximates normality in the center with outliers.
K-means clustering improved for robustness to outliers and distribution shifts.
problem K-means is brittle to outliers, distribution shifts, and limited samples.
method Developed a distributionally robust variant using Wasserstein-2 ball around the empirical distribution.
result Substantial gains in outlier detection and robustness to noise demonstrated.
Proposes CE-BASS for robust Kalman filtering with innovative and additive outliers.
problem Robustness to both innovative and additive outliers in Kalman filtering.
method Particle mixture Kalman filter with re-sampling of past states.
result CE-BASS efficiently handles multi-modality and trend changes in hidden state distributions.
This paper presents a simple but effective density-based outlier detection approach with the local kernel density estimation (KDE). A Relative Density-based Outlier Score (RDOS) is introduced to measure the local outlierness of objects, in which the density distribution at the location of an object is estimated with a …
A new robust Wasserstein distance is proposed to handle outliers in probability distributions.
problem Outliers in probability distributions make Wasserstein distances sensitive and impractical.
method Introduces a new outlier-robust Wasserstein distance Wpε. result Achieves strong robust estimation guarantees under the Huber ε-contamination model. Proposes BCOPS for balanced and outlier detection in multi-class classification.
problem Classification problems with different training and test distributions.
method BCOPS combines supervised learning with conformal prediction to optimize out-of-sample performance and detect outliers.
result BCOPS constructs prediction sets with finite-sample coverage guarantees and outlier detection rate estimation.
Paper uses genome Markov structure for outlier detection and read classification.
problem Identifying outliers and classifying reads in genome databases.
method Applying second-order Markov models to triplet base distributions.
result Improved accuracy in outlier identification and read classification.
Study improves robustness and sparsity in linear regression with adversarial outliers and heavy-tailed noise.
problem Outliers and heavy-tailed noise in linear regression coefficients.
method Sharp concentration inequalities and generic chaining.
result Sharper error bounds under weaker assumptions.
DORO improves DRO's performance and stability in tasks with subpopulation shift.
problem DRO's poor performance and instability in tasks with subpopulation shift.
method DORO, a refined risk function that prevents overfitting to outliers.
result DORO improves DRO's performance and stability on large modern datasets.
Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are different from the majority. While many statistical learning and data mining techniques have been used for developing more effective outlier detection algorithms, the interpretation of detected outliers do…
The paper finds the normal distribution unsuitable for modeling daily stock returns and suggests using the Laplace distribution instead.
problem The difficulty in modeling the distribution of daily stock returns, especially for extreme outliers.
method Investigation of daily stock returns of major indices using both normal and Laplace distributions.
result The normal distribution is not a good model for stock returns, even over long periods of data.
RFPCA improves robustness of FPCA for matrix data.
problem Outliers in matrix data degrade the performance of FPCA.
method RFPCA uses matrix-variate t-distribution and EM algorithm for robust estimation.
result RFPCA outperforms other methods in detecting matrix-valued outliers.
COPOD detects outliers efficiently and interpretable using copulas.
problem Outliers in multivariate data are hard to detect efficiently and interpretably.
method COPOD constructs an empirical copula to predict tail probabilities and identify outliers.
result COPOD outperforms existing methods in most cases and is computationally efficient.
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…
iSearch uses innovation directions for robust PCA and outlier detection.
problem Robust PCA and outlier detection in data with outliers.
method iSearch uses innovation directions to compute optimal data points and identify outliers.
result iSearch provides robust PCA and outlier detection with performance guarantees.
Identifies root causes of outliers using causal DAGs.
problem Detecting and understanding the causes of anomalies in interrelated variables.
method Formal approach using causal directed acyclic graphs (DAGs), outlier scores, and Shapley values.
result Quantifies the extent of outlier scores attributed to ancestors.
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…
A robust Gaussian process model using Huber likelihood for outlier resistance.
problem Outliers in observational data sets affect Gaussian process regression's robustness.
method Proposes a Gaussian process model with Huber likelihood and weights based on projection statistics.
result Demonstrates improved statistical efficiency and robustness to outliers.
Novel conformal methods test out-of-distribution data with labeled outliers.
problem Testing whether new data comes from the same distribution as a reference.
method Integrative conformal p-values re-weight standard p-values using dependent side information.
result The methods outperform standard conformal p-values in simulations and applications.
KOD detects outliers in high-dimensional data.
problem Challenges in outlier detection in high-dimensional settings.
method Kernel transformation followed by projection pursuit approach with ensemble of directions and result combination.
result Empirical evaluations show effectiveness on various datasets.
RVAE detects and repairs corrupted cells in mixed-type tabular data.
problem Outlier detection and repair in mixed-type tabular data.
method Robust Variational Autoencoder (RVAE) learns the joint distribution of clean data and identifies outlier cells.
result RVAE outperforms state-of-the-art methods in cell outlier detection and repair for tabular data.
Outliers are ubiquitous in modern data sets. Distance-based techniques are a popular non-parametric approach to outlier detection as they require no prior assumptions on the data generating distribution and are simple to implement. Scaling these techniques to massive data sets without sacrificing accuracy is a challeng…
One-Class Boundary Peeling detects outliers efficiently and robustly.
problem Unsupervised outlier detection in diverse data distributions.
method One-Class Boundary Peeling uses flexible boundaries generated by one-class SVMs and iteratively peels them.
result One-Class Boundary Peeling outperforms state-of-the-art methods in synthetic data simulations.
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…
Quantum entropy scoring improves robust mean estimation and outlier detection.
problem Robust mean estimation and outlier detection in high-dimensional data.
method QUE-scoring based on quantum entropy regularization.
result First algorithm with optimal error rates and nearly-linear running time for robust mean estimation.
OutlierTree detects outliers using decision trees and provides explanations.
problem Detecting outliers in data while providing understandable explanations.
method Supervised decision tree splits with 1-d confidence intervals.
result Human-readable explanations for outlier detection.
Develops metrics to identify outliers in machine learning datasets.
problem Identifying outliers in machine learning datasets.
method Five methods to score examples based on well-representedness.
result All methods correlate highly, suggesting a quantifiable notion of well-representedness.
A robust model handles up to 25% of outliers in time-series data for power flow calculations.
problem Handling outliers in time-series data for accurate power flow calculations.
method Robust data-driven process model with Schweppe-type generalized maximum likelihood estimator and projection statistics for outlier weighting.
result The model can handle up to 25% of outliers in the training data set.
Paper improves anomaly detection by using non-uniform random choices in isolation forests.
problem Detecting clustered diverse outliers more effectively.
method Comparing different split guiding criteria in isolation forests.
result Non-uniform random choices improve outlier discrimination for certain outlier classes.
A new KF handles outliers without MSE loss.
problem Outliers degrade Kalman filter performance.
method NUV priors, EM and AM for variance estimation.
result Outlier-insensitive KF outperforms existing methods.
The IM effect helps detect anomalies by memorizing inliers early.
problem Challenges in fully unsupervised outlier detection.
method Theoretical study of a simple autoencoder, focusing on early training dynamics.
result Characterization of IM effect emergence, strength, and persistence.
We develop efficient algorithms for robust PCA that handle outliers.
problem Finding principal components in datasets with outliers.
method Nearly-linear time and streaming algorithms for robust PCA.
result Near-optimal error guarantees for robust PCA with nearly-linear time and memory usage.