A new method for multi-target regression robust to outliers.
problem Outliers in regression data affect the performance of ELM-based methods.
method Generalized Outlier Robust ELM (GOR-ELM) using ℓ2,1 norm and Elastic Net theory. result GOR-ELM outperforms other ELM-based techniques in datasets with outliers.
Proposes MSD-Kmeans for efficient outlier detection.
problem Detecting unusual records in noisy data.
method Combines MSD and K-means for more accurate outlier detection.
result MSD-Kmeans achieves highest precision, accuracy, and F-measure.
XGBOD combines unsupervised and supervised methods for better outlier detection.
problem Enhanced outlier detection from normal observations in various datasets.
method Hybrid approach using unsupervised representation learning to improve a supervised classifier.
result XGBOD outperforms competing methods across seven datasets.
Advances in sensor technology have enabled the collection of large-scale datasets. Such datasets can be extremely noisy and often contain a significant amount of outliers that result from sensor malfunction or human operation faults. In order to utilize such data for real-world applications, it is critical to detect ou…
New method detects business-relevant outliers in e-commerce conversion rates.
problem Identifying outliers in e-commerce conversion rate data.
method A novel unsupervised fluid IQR method that adjusts sensitivity based on platform activity.
result Fluid IQR method outperforms existing methods in business-relevance and robustness.
A new robust GP regression algorithm that trims outliers improves model accuracy.
problem Severe bias in GP regression due to data contamination by outliers.
method Iterative trimming of extreme data points.
result Significantly outperforms standard and robust GP variants in most test cases.
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…
Rare data in a large-scale database are called outliers that reveal significant information in the real world. The subspace-based outlier detection is regarded as a feasible approach in very high dimensional space. However, the outliers found in subspaces are only part of the true outliers in high dimensional space, in…
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.
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.
New algorithm robust to outliers in Bayesian Optimization.
problem Vulnerability of Bayesian Optimization to extreme outliers.
method Introduces a new adversary with a frequency-bounded corruption budget and derives RCGP-UCB algorithm.
result Achieves sublinear regret in the presence of up to O(T1/4) and O(T1/7) corruptions with possibly infinite magnitude. 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…
DORA analyzes deep neural networks' internal representations to detect spurious correlations.
problem Detecting spurious correlations in deep neural networks' internal representations.
method DORA uses Extreme-Activation (EA) distance measure to assess representation similarities.
result Identifies internal representations capable of detecting spurious correlations.
Many traditional methods for identifying changepoints can struggle in the presence of outliers, or when the noise is heavy-tailed. Often they will infer additional changepoints in order to fit the outliers. To overcome this problem, data often needs to be pre-processed to remove outliers, though this is difficult for a…
The problem of outlier detection is extremely challenging in many domains such as text, in which the attribute values are typically non-negative, and most values are zero. In such cases, it often becomes difficult to separate the outliers from the natural variations in the patterns in the underlying data. In this paper…
Any research on strategies for reaching business excellence aims at revealing the appropriate course of actions any executive should consider. Thus, discussions take place on how effective a performance measurement system can be estimated, or/and validated. Can one find an adequate measure (i) on the performance result…
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.
Improved LDA with capped l_{2,1}-norm reduces outlier sensitivity.
problem Outliers and noise sensitivity in classical LDA.
method Introducing capped l_{2,1}-norm and proposing CLDA.
result CLDA effectively removes outliers and suppresses noise.
DTOR explains anomalies with rule-based explanations.
problem Need to explain anomalies in data effectively.
method Applies Decision Tree Regressor to estimate anomaly scores and generate rule-based explanations.
result DTOR produces robust and consistent rule-based explanations.
This paper solves the normalizability crisis in sequential inference by introducing bounded information geometry.
problem Structural failure in standard sequential inference architectures when dealing with extreme outliers.
method Non-parametric field actions and bounded information geometry to truncate infinite tails of spatial distributions.
result Empirical benchmarks across three domains show robust estimation without infinite-tailed distributional assumptions.
Identifies arms with rewards significantly different from the majority.
problem Identifying arms with rewards deviating substantially from the majority.
method Computing outlier threshold using median and median absolute deviation.
result Proposes two δ-PAC algorithms for ROAI with upper bounds on sample complexity and worst case lower bound.
FLANDERS detects and blocks extreme model poisoning in federated learning.
problem Resilience against large-scale model poisoning attacks in federated learning.
method FLANDERS treats client updates as matrix-valued time series and identifies outliers using autoregressive forecasting.
result FLANDERS significantly improves robustness in federated learning across various attacks.
Study examines extreme and erratic cryptocurrency behaviour during COVID-19.
problem Analyse extreme and erratic cryptocurrency behaviour during the pandemic.
method Analyze distribution extremities and structural breaks in 51 cryptocurrencies.
result Identify cryptocurrencies with most irregular extreme and erratic behaviour.
Automates OD model selection via meta-learning.
problem Selecting effective OD models for new datasets.
method Meta-learning approach based on past model performances.
result MetaOD selects models significantly outperforming existing methods.
WPCA improves subspace recovery robustness to outliers.
problem Improving subspace recovery in the presence of outliers.
method Winsorized PCA (WPCA) with theoretical analysis of accuracy and robustness.
result WPCA provides consistent subspace recovery from contaminated data.
Mean embeddings provide an extremely flexible and powerful tool in machine learning and statistics to represent probability distributions and define a semi-metric (MMD, maximum mean discrepancy; also called N-distance or energy distance), with numerous successful applications. The representation is constructed as the e…
ODBAE detects complex phenotypes in biological data.
problem Challenges in identifying complex phenotypes from high-dimensional biological data.
method ODBAE (Outlier Detection using Balanced Autoencoders) identifies influential and high leverage points in latent relationships among multiple physiological parameters.
result ODBAE reveals novel metabolism-related genes and uncovers coordinated abnormalities across metabolic indicators.
A new robust scaling approach improves downstream metabolomics analysis.
problem Challenges in choosing scaling techniques for metabolomics data.
method Introduces a weighted scaling approach robust to outliers.
result The proposed method outperforms traditional scaling techniques in both outlier-free and outlier-present datasets.
New method improves robustness and efficiency of inference from complex models.
problem Inability of existing methods to handle outliers in simulator-based models.
method Generalised Bayesian inference with neural approximation of weighted score-matching loss.
result Provable robustness to outliers and computational efficiency.
Finding rare information hidden in a huge amount of data from the Internet is a necessary but complex issue. Many researchers have studied this issue and have found effective methods to detect anomaly data in low dimensional space. However, as the dimension increases, most of these existing methods perform poorly in de…
Novel LRMC tackles missing data and outliers in large-scale low-rank data recovery.
problem Missing data and extreme outliers in low-rank data analysis.
method Learned Robust Matrix Completion (LRMC) using deep unfolding and flexible neural network framework.
result LRMC achieves optimum performance with low computational complexity and linear convergence.
A new algorithm selects data subsets avoiding outliers and high leverage points.
problem Outliers and high leverage points skew model estimates in subsamples.
method Unsupervised and supervised exchange procedures to select nearly D-optimal subsets.
result The new methods improve model accuracy by avoiding influential points.
Identifying the unknown underlying trend of a given noisy signal is extremely useful for a wide range of applications. The number of potential trends might be exponential, which can be computationally exhaustive even for short signals. Another challenge, is the presence of abrupt changes and outliers at unknown times w…
Exponential Lasso improves Lasso's robustness to outliers and heavy-tailed noise.
problem Lasso's sensitivity to outliers and heavy-tailed noise in high-dimensional statistics.
method Integrates an exponential-type loss function into the Lasso framework.
result Achieves strong statistical convergence rates robust to heavy-tailed contamination.
DFRot improves LLMs by reducing outlier and massive activation effects.
problem Reducing outlier and massive activation effects in rotated LLMs.
method Weighted loss function and orthogonal Procrustes transforms for rotation matrix refinement.
result DFRot achieves dual free (Outlier-Free and Massive Activation-Free) with significant improvements in perplexity.
Online active learning improves model performance in high-volume production processes.
problem Outliers degrade the predictive performance of online active learning models.
method Proposes a robust estimator and bounds the search area of a conditional D-optimal algorithm.
result The proposed method improves the performance of online active learning in the presence of outliers.
This study presents a long-term alternative formula for stock price variation described by a geometric Brownian motion on the basis of median instead of mean or expected values. The proposed method is motivated by the observation made in remote fields, where optimality of bet-hedging or diversification strategies is ex…
Paper formalizes continual semi-supervised anomaly detection, showing promising results.
problem Formalizing continual semi-supervised anomaly detection in real-world conditions.
method Baseline model of variational autoencoder (VAE) with deep generative replay and outlier rejection.
result Outlier rejection shows promising results, often surpassing baseline methods.
Support Vector Data Description (SVDD) is a popular outlier detection technique which constructs a flexible description of the input data. SVDD computation time is high for large training datasets which limits its use in big-data process-monitoring applications. We propose a new iterative sampling-based method for SVDD…
A taxonomy of large financial crashes proposed in the literature locates the burst of speculative bubbles due to endogenous causes in the framework of extreme stock market crashes, defined as falls of market prices that are outlier with respect to the bulk of drawdown price movement distribution. This paper goes on dee…
We investigate the distributions of epsilon-drawdowns and epsilon-drawups of the most liquid futures financial contracts of the world at time scales of 30 seconds. The epsilon-drawdowns (resp. epsilon- drawups) generalise the notion of runs of negative (resp. positive) returns so as to capture the risks to which invest…
Predicts arterial road incident duration with extreme gradient boosting.
problem Predicting incident duration on arterial roads, especially with limited data.
method Bi-level framework combining classification and regression models.
result Extreme gradient boosting outperformed other models by 53%.
Bayesian econometrics improves nowcasting during pandemics.
problem Improving nowcasting during extreme economic events like pandemics.
method Bayesian econometric methods using non-parametric mixed frequency VARs with additive regression trees.
result Significant improvements in nowcasting performance compared to linear models.
Christoffel function characterizes the corruption a bounded-degree certificate cannot remove in robust halfspace learning.
problem Robust halfspace learning under malicious noise
method Sum-of-Squares degree of outlier-removal certificate
result Christoffel function bounds the corruption a bounded-degree certificate cannot remove
Introduces RMEE for robust classification, improving MEE's performance in noisy conditions.
problem Improving robustness of MEE criterion for noisy classification.
method Analyzed optimal error distribution, introduced RMEE with half-quadratic optimization.
result RMEE achieves better robustness in noisy conditions compared to original MEE.
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.
Existing strategies for finite-armed stochastic bandits mostly depend on a parameter of scale that must be known in advance. Sometimes this is in the form of a bound on the payoffs, or the knowledge of a variance or subgaussian parameter. The notable exceptions are the analysis of Gaussian bandits with unknown mean and…
We propose an inlier-based outlier detection method capable of both identifying the outliers and explaining why they are outliers, by identifying the outlier-specific features. Specifically, we employ an inlier-based outlier detection criterion, which uses the ratio of inlier and test probability densities as a measure…