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New GPLFR model predicts high-dimensional outputs from sparse data

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Gaussian Process Latent Factor Regression (GPLFR), a new model designed to predict high-dimensional outputs from limited training data. This method addresses limitations in existing multi-output Gaussian processes and compress-then-predict pipelines by integrating compression and prediction into a single objective. GPLFR has been successfully applied to create the first spatially resolved emulator for global climate models of rocky exoplanets. AI

IMPACT Introduces a novel method for handling complex prediction tasks with limited data, potentially advancing scientific modeling.

RANK_REASON This is a research paper describing a new model.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Edward T. Stevenson, Eric T. Wolf, Mei Ting Mak, N. J. Mayne, Miles Cranmer ·

    Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems

    arXiv:2606.06576v1 Announce Type: cross Abstract: In the sciences, regression tasks often require predicting high-dimensional outputs from few training examples. Multi-output Gaussian processes excel in low-data regimes but typically struggle with high-dimensional outputs. Compre…

  2. arXiv stat.ML TIER_1 English(EN) · Miles Cranmer ·

    Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems

    In the sciences, regression tasks often require predicting high-dimensional outputs from few training examples. Multi-output Gaussian processes excel in low-data regimes but typically struggle with high-dimensional outputs. Compress-then-predict pipelines such as PCA-GP (principa…

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