A new mixture of experts model using shifted asymmetric Laplace distributions for robust regression and clustering of asymmetric heavy-tailed data, fitted via a hybrid EM-MM algorithm.
Springer, New York (2006)
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Derives dataset-specific theoretical and human-like QWK ceilings for AES from classical test theory reliability, showing that human-human QWK often underestimates the true achievable limit.
ProtoCLIP improves zero-shot chest X-ray classification in CLIP models by 2-10 AUC points via curated data and prototype-aligned distillation, reaching 0.94 AUC for pneumothorax on VinDr-CXR.
An unsupervised-to-supervised ML pipeline on UK NDNS data discovers four dietary patterns, reproduces them with macro-F1 0.963 using a surrogate classifier, and interprets them via SHAP for potential clinical use.
citing papers explorer
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Shifted asymmetric Laplace mixtures of experts
A new mixture of experts model using shifted asymmetric Laplace distributions for robust regression and clustering of asymmetric heavy-tailed data, fitted via a hybrid EM-MM algorithm.
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Has Automated Essay Scoring Reached Sufficient Accuracy? Deriving Achievable QWK Ceilings from Classical Test Theory
Derives dataset-specific theoretical and human-like QWK ceilings for AES from classical test theory reliability, showing that human-human QWK often underestimates the true achievable limit.
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ProtoCLIP: Prototype-Aligned Latent Refinement for Robust Zero-Shot Chest X-Ray Classification
ProtoCLIP improves zero-shot chest X-ray classification in CLIP models by 2-10 AUC points via curated data and prototype-aligned distillation, reaching 0.94 AUC for pneumothorax on VinDr-CXR.
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An Explainable Unsupervised-to-Supervised Machine Learning Framework for Dietary Pattern Discovery Using UK National Dietary Survey Data
An unsupervised-to-supervised ML pipeline on UK NDNS data discovers four dietary patterns, reproduces them with macro-F1 0.963 using a surrogate classifier, and interprets them via SHAP for potential clinical use.