Pandora's Regret is a closed-form pairwise scoring rule derived from expected optimal search costs that elicits true probabilities and outperforms log loss, accuracy, and F1 at predicting diagnostic costs on MedMNIST models.
ISBN 1595933832
6 Pith papers cite this work. Polarity classification is still indexing.
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K-DSM uses per-feature kurtosis to set noise scales in DSM, enabling effective single-scale anomaly detection on tabular benchmarks in both semi-supervised and unsupervised settings.
The paper develops a design science framework for governing AI-assisted operational decision support in security operations centers by specifying a query-broker artifact that separates AI planning from execution through approved templates, policy validation, and engineering review gates.
Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.
A supervision construction procedure generates explicit support and controlled non-support examples (counterfactual and topic-related negatives) without manual annotation, producing verifiers that demonstrate genuine evidence dependence in radiology tasks.
GARG-AML detects smurfing in anti-money laundering using second-order graph neighborhood densities combined with machine learning classifiers for scalable and interpretable results.
citing papers explorer
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Pandora's Regret: A Proper Scoring Rule for Evaluating Sequential Search
Pandora's Regret is a closed-form pairwise scoring rule derived from expected optimal search costs that elicits true probabilities and outperforms log loss, accuracy, and F1 at predicting diagnostic costs on MedMNIST models.
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Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection
K-DSM uses per-feature kurtosis to set noise scales in DSM, enabling effective single-scale anomaly detection on tabular benchmarks in both semi-supervised and unsupervised settings.
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Governing AI-Assisted Security Operations: A Design Science Framework for Operational Decision Support
The paper develops a design science framework for governing AI-assisted operational decision support in security operations centers by specifying a query-broker artifact that separates AI planning from execution through approved templates, policy validation, and engineering review gates.
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Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework
Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.
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Case-Grounded Evidence Verification: A Framework for Constructing Evidence-Sensitive Supervision
A supervision construction procedure generates explicit support and controlled non-support examples (counterfactual and topic-related negatives) without manual annotation, producing verifiers that demonstrate genuine evidence dependence in radiology tasks.
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GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering
GARG-AML detects smurfing in anti-money laundering using second-order graph neighborhood densities combined with machine learning classifiers for scalable and interpretable results.