SOTAVerified

Outlier Detection

Outlier Detection is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.

Source: Coverage-based Outlier Explanation

Papers

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Showing 1–10 of 703 papers

TitleStatusHype
Robust Spatiotemporal Epidemic Modeling with Integrated Adaptive Outlier DetectionCode0
Universal Embeddings of Tabular Data—0
Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles—0
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with OutliersCode0
LayerIF: Estimating Layer Quality for Large Language Models using Influence Functions—0
Learning novel representations of variable sources from multi-modal Gaia data via autoencoders—0
Re-experiment Smart: a Novel Method to Enhance Data-driven Prediction of Mechanical Properties of Epoxy Polymers—0
Importance Sampling for Nonlinear ModelsCode0
Robust Indoor Localization via Conformal Methods and Variational Bayesian Adaptive Filtering—0
Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks SafetyCode0
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All datasetsECG5000Balance scale_class 1Breast cancer Wisconsin_class 2Breast cancer Wisconsin_class 4Fashion-MNISTGlass identificationHeart-CHepatitisInternet AdIonosphere_class bSKAB

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy37.62—Unverified