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 1–25 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
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach—0
Comparison of Visual Trackers for Biomechanical Analysis of Running—0
Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest—0
A probabilistic view on Riemannian machine learning models for SPD matrices—0
Unsupervised outlier detection to improve bird audio dataset labels—0
Adaptive PCA-Based Outlier Detection for Multi-Feature Time Series in Space Missions—0
Adversarial Subspace Generation for Outlier Detection in High-Dimensional DataCode0
Robust Randomized Low-Rank Approximation with Row-Wise Outlier Detection—0
Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies—0
TRIDIS: A Comprehensive Medieval and Early Modern Corpus for HTR and NER—0
PS-EIP: Robust Photometric Stereo Based on Event Interval Profile—0
Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization—0
Highly Efficient Direct Analytics on Semantic-aware Time Series Data Compression—0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
OuroMamba: A Data-Free Quantization Framework for Vision Mamba Models—0
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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
1VRAE+SVMAccuracy0.98—Unverified
2F-t ALSTM-FCNAccuracy0.95—Unverified
3GENDISAccuracy0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.03—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy37.62—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy65.6—Unverified
#ModelMetricClaimedVerifiedStatus
1PAEAUROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.05—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC0.86—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy86.33—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMCapsAverage F10.74—Unverified