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Creators/Authors contains: "Ye, Chenglong"

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  1. ; ; ; (, Journal of Machine Learning for Modeling and Computing)
    Overparameterized models like deep nets and random forests have become very popular in machine learning. However, the natural goals of continuity and differentiability, common in regression models, are now often ignored in modern overparametrized, locally adaptive models. We propose a general framework to construct a global continuous and differentiable model based on a weighted average of locally learned models in corresponding local regions. This model is competitive in dealing with data with different densities or scales of function values in different local regions. We demonstrate that when we mix kernel ridge and polynomial regression terms in the local models, and stitch themtogether continuously, we achieve faster statistical convergence in theory and improved performance in various practical settings. 
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  2. ; ; (, Journal of Computational and Graphical Statistics)
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  3. ; ; ; (, Nature Geoscience)
    null (Ed.)
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  4. ; ; ; (, Environmental Data Initiative)
    To understand controls on soil organic matter chemical composition across North America, we collected 13C NMR spectra and conducted and synthesized additional biogeochemical measurements from NEON Megapit soil samples as well as additional samples (total n = 42). This dataset supports the findings described in the associated manuscript by Hall, Ye et al. (2020). 
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