MacrOData supplies three large, curated benchmark suites totaling 2,446 datasets for tabular outlier detection, complete with standardized splits, metadata, and a public leaderboard.
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Breunig, Hans-Peter Kriegel, Raymond T
9 Pith papers cite this work, alongside 3,862 external citations. Polarity classification is still indexing.
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POST uses prior-observation adversarial learning on adjacency matrices to reduce spatial over-generalization in graph-based multivariate time series anomaly detection and achieves new SOTA results on detection and channel-wise localization.
A novel unsupervised anomaly detection method for time series using Haar wavelets and a designed t-test outperforms state-of-the-art benchmarks across 343 datasets.
Coupling-Grouped XY-QAOA enables joint anomaly-feature selection via a constraint-preserving grouped-angle QAOA variant, achieving 45.9-61.3% circuit depth reduction and larger feasible executions (64 qubits at p=2) on IBM Heron hardware compared to standard approaches.
ExDBSCAN generates multiple counterfactual explanations for DBSCAN assignments via a density-connected weighted graph and physics-inspired model, achieving perfect validity and outperforming baselines on 30 tabular datasets.
Diffusion models via DDPM work for anomaly detection but are slow; the proposed DTE method estimates diffusion time distribution analytically and with a neural net to deliver faster inference while outperforming DDPM on ADBench for unsupervised and semi-supervised settings.
Unsupervised UMAP plus Friends-of-Friends clustering on 58 million DESI DR2 spectra identifies 1.1 million outlier candidates where visual checks show 67 percent have reduction artifacts missed by the standard pipeline.
A systematic literature survey that classifies data-driven KPI prediction methods for 6G networks across KPI type, data source, protocol stack layer, horizon, model family, and objective.
PaAno+ extends the original PaAno with multiscale feature extraction, cross-variable fusion attention, and a temporal patch sorting pretext task to report state-of-the-art results on the TSB-AD benchmark for univariate and multivariate anomaly detection.
citing papers explorer
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MacrOData: New Benchmarks of Thousands of Datasets for Tabular Outlier Detection
MacrOData supplies three large, curated benchmark suites totaling 2,446 datasets for tabular outlier detection, complete with standardized splits, metadata, and a public leaderboard.
-
POST: Prior-Observation Adversarial Learning of Spatio-Temporal Associations for Multivariate Time Series Anomaly Detection
POST uses prior-observation adversarial learning on adjacency matrices to reduce spatial over-generalization in graph-based multivariate time series anomaly detection and achieves new SOTA results on detection and channel-wise localization.
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Fast and Accurate Anomaly Detection in Time Series
A novel unsupervised anomaly detection method for time series using Haar wavelets and a designed t-test outperforms state-of-the-art benchmarks across 343 datasets.
-
Coupling-Grouped XY-QAOA for Joint Anomaly-Feature Selection
Coupling-Grouped XY-QAOA enables joint anomaly-feature selection via a constraint-preserving grouped-angle QAOA variant, achieving 45.9-61.3% circuit depth reduction and larger feasible executions (64 qubits at p=2) on IBM Heron hardware compared to standard approaches.
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ExDBSCAN: Explaining DBSCAN with Counterfactual Reasoning -- Additional Material
ExDBSCAN generates multiple counterfactual explanations for DBSCAN assignments via a density-connected weighted graph and physics-inspired model, achieving perfect validity and outperforming baselines on 30 tabular datasets.
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On Diffusion Modeling for Anomaly Detection
Diffusion models via DDPM work for anomaly detection but are slow; the proposed DTE method estimates diffusion time distribution analytically and with a neural net to deliver faster inference while outperforming DDPM on ADBench for unsupervised and semi-supervised settings.
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Quality Assessment of Spectroscopic Data Reduction Pipelines Using Artificial Intelligence: Scrutinizing Data Release 2 from the DESI Survey
Unsupervised UMAP plus Friends-of-Friends clustering on 58 million DESI DR2 spectra identifies 1.1 million outlier candidates where visual checks show 67 percent have reduction artifacts missed by the standard pipeline.
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AI-Based KPI Prediction Methods in Future 6G Networks: A Survey
A systematic literature survey that classifies data-driven KPI prediction methods for 6G networks across KPI type, data source, protocol stack layer, horizon, model family, and objective.
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PaAno+: Multiscale Encoding and Cross-Variable Attention for Time Series Anomaly Detection
PaAno+ extends the original PaAno with multiscale feature extraction, cross-variable fusion attention, and a temporal patch sorting pretext task to report state-of-the-art results on the TSB-AD benchmark for univariate and multivariate anomaly detection.