Kernel Embeddings of Latent Tree Graphical Models

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Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “Conditional mean embedding has enjoyed successful applications in graphical models and probabilistic inference (Song et al. 2009; 2010a; 2011a;b; 2010b).”
Kernel Mean Shrinkage Estimators (2014, methodology): “The same trick can be easily generalized to tensors of higher order, which have been previously used, for example, in Song et al. (2011).”
Kernel Embeddings of Conditional Distributions: A Unified Kernel Framework for Nonparametric Inference in Graphical Models (2013, methodology): “In this paper, we will review the recent development of nonparametric inference with kernel embeddings of conditional distributions [4, 5, 6, 7, 8, 9, 10, 11, 12], and provide a novel, unified kernel framework for addressing a broad class of challenging problems in graphical models.”
Nonparametric Estimation of Multi-View Latent Variable Models (2013, background): “Recently, there is a surge of interest in designing spectral algorithms for estimating the parameters of latent variable models (Hsu et al., 2009; Parikh et al., 2011; Song et al., 2011; Foster et al., 2012; Anandkumar et al., 2012a,b; Kir´aly, 2013).”