GPLFR jointly learns compression and GP regression for high-dimensional outputs, outperforming PCA-GP under structured noise and enabling a spatially resolved rocky-exoplanet climate emulator.
Prediction by Supervised Principal Components
3 Pith papers cite this work, alongside 830 external citations. Polarity classification is still indexing.
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Pith papers citing it
830
external citations · Crossref
representative citing papers
A density-ratio framework compresses BMA posteriors into hard or soft support regions with explicit TV, KL, and predictive distortion bounds under predictor redundancy.
CSPCA combines response covariance and variance into one trace objective, yielding a closed-form projection via the top eigenvectors of X^T YY^T X + κ X^T X.
citing papers explorer
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Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems
GPLFR jointly learns compression and GP regression for high-dimensional outputs, outperforming PCA-GP under structured noise and enabling a spatially resolved rocky-exoplanet climate emulator.
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Bayesian Model Averaging under Predictor Redundancy via Density-Ratio Posterior Compression
A density-ratio framework compresses BMA posteriors into hard or soft support regions with explicit TV, KL, and predictive distortion bounds under predictor redundancy.
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Covariance Supervised Principal Component Analysis
CSPCA combines response covariance and variance into one trace objective, yielding a closed-form projection via the top eigenvectors of X^T YY^T X + κ X^T X.