UD-DML creates balanced representative subsamples via uniform design in PCA space for efficient double machine learning estimation of average treatment effects on large datasets.
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Multiscale CMH scanning generalizes the classic test to continuous spaces, achieving consistency for conditional independence testing by conditioning on marginal order statistics without requiring large stratum sizes.
SPENDER autoencoder plus k-d tree nearest-neighbor classification on DESI spectra identifies AGN and broad-line AGN at accuracies 0.952 and 0.965, recovering sources missed by single-line diagnostics.
Multi-query vector retrieval via anomalous pattern detection on shared dimensions improves retrieval as query-set size grows, especially from 1 to 8 queries.
A reference-free proxy scoring framework combined with GIRB calibration produces better-aligned evaluation metrics for summarization and outperforms baselines across seven datasets.
citing papers explorer
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UD-DML: Uniform Design Subsampling for Double Machine Learning over Massive Data
UD-DML creates balanced representative subsamples via uniform design in PCA space for efficient double machine learning estimation of average treatment effects on large datasets.
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Multiscale Cochran-Mantel-Haenszel Scanning for Conditional Dependency
Multiscale CMH scanning generalizes the classic test to continuous spaces, achieving consistency for conditional independence testing by conditioning on marginal order statistics without requiring large stratum sizes.
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Beyond traditional emission-line diagnostics: using autoencoders to uncover active galactic nuclei in DESI spectra
SPENDER autoencoder plus k-d tree nearest-neighbor classification on DESI spectra identifies AGN and broad-line AGN at accuracies 0.952 and 0.965, recovering sources missed by single-line diagnostics.
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Retrieval with Multiple Query Vectors through Anomalous Pattern Detection
Multi-query vector retrieval via anomalous pattern detection on shared dimensions improves retrieval as query-set size grows, especially from 1 to 8 queries.
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Calibrating Model-Based Evaluation Metrics for Summarization
A reference-free proxy scoring framework combined with GIRB calibration produces better-aligned evaluation metrics for summarization and outperforms baselines across seven datasets.