Density-based outliers in labor market text act as leading indicators of new occupational clusters, with an extended Emerging Occupation Score predicting formation 2 quarters ahead at F1=0.74 on 84,988 postings.
Breunig, Hans-Peter Kriegel, Raymond T
10 Pith papers cite this work. Polarity classification is still indexing.
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2026 10representative citing papers
Latent SDE generative model for anomaly detection in sparse irregular multivariate time series outperforms baselines on six benchmarks and stays robust under severe sparsity.
A metric learning method is introduced to learn distance metrics that best capture conditional anomaly patterns in instance-based detection.
An LLM agent automates iterative refinement of data embedding visualizations by generating semantic evaluation reports and recommending configuration changes.
Four-planet systems exhibit exponentially increasing lifetimes with orbital spacing, intermediate between three- and five-planet systems, with resonances causing shorter lifetimes and third-order MMRs adding destabilization near certain spacings.
Active learning with masked reconstruction and minimax training raises AUC by 12.39% across 28 test cases on four multivariate datasets and seven unsupervised backbones.
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Instance-based conditional anomaly detection with optimized distance metrics detects unusual patient-management decisions in two real-world medical datasets.
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citing papers explorer
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Noise is Signal: Density-Based Outliers as Leading Indicators of Occupational Emergence in Labor Market Text
Density-based outliers in labor market text act as leading indicators of new occupational clusters, with an extended Emerging Occupation Score predicting formation 2 quarters ahead at F1=0.74 on 84,988 postings.
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Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs
Latent SDE generative model for anomaly detection in sparse irregular multivariate time series outperforms baselines on six benchmarks and stays robust under severe sparsity.
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Distance metric learning for conditional anomaly detection
A metric learning method is introduced to learn distance metrics that best capture conditional anomaly patterns in instance-based detection.
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Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning
Active learning with masked reconstruction and minimax training raises AUC by 12.39% across 28 test cases on four multivariate datasets and seven unsupervised backbones.
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Conditional anomaly detection methods for patient-management alert systems
Instance-based conditional anomaly detection with optimized distance metrics detects unusual patient-management decisions in two real-world medical datasets.