Nonparametric Tree Graphical Models

By Le Song, Arthur Gretton, Carlos Guestrin. 2010

50 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Probabilistic & Bayesian Methods

Citations in the corpus

Discriminative Embeddings of Latent Variable Models for Structured Data (2016, neutral): “Similar to kernel BP (Song et al., 2010, 2011) and kernel EP (Jitkrittum et al., 2015), our current work exploits feature space embedding to reformulate graphical model inference procedures.”
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages (2015, background): “We note that Song et al. (2010, 2011) propose a related regression approach for predicting outgoing messages from incoming messages, for the purpose of belief propagation.”
Modelling transition dynamics in MDPs with RKHS embeddings (2012, methodology): “Suppose K is a bounded kernel and the conditions of (Song et al., 2010b) e ) , and thus by choosing λ → 0 , λ 3 m → ∞ we have that, for any ǫ > 0 , By the reproducing property of L , we have In this work, for theoretical analysis, we consider a normalised version of (3): where b .”
Conditional mean embeddings as regressors (2012, methodology): “Following (Song et al., 2009; 2010), we deﬁne a second kernel K : X × X → R with associated RKHS H K , and consider the estimate where , and where =1 , and λ is a chosen regularization parameter.”
Kernel Belief Propagation (2011, methodology): “, 2009), which generalizes an earlier kernel algorithm for exact inference on trees (Song et al., 2010).”
Recovering Distributions from Gaussian RKHS Embeddings (2014, background): “…Gretton et al., 2008) to machine learning problems including state-space modeling (Song et al., 2009; Fukumizu et al., 2011), belief propagation (Song et al., 2010; Song et al., 2011), predictive state representations (Boots et al., 2013), and reinforcement learning (Grünewälder et al., 2012;…”
Monte Carlo Filtering Using Kernel Embedding of Distributions (2014, methodology): “…such as statistical testing (Gretton et al. 2012; 2008), time series analysis (Song et al. 2009; 2010; Fukumizu, Song, and Gretton 2011), belief propagation (Song, Gretton, and Guestrin 2010; Song et al. 2011), and reinforcement learning (Grünewälder et al. 2012b; Nishiyama et al. 2012).”
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.”
Kernel Embeddings of Latent Tree Graphical Models (2011, background): “In other words, the covariance operator is also the embedding of the joint distribution P(Xs, Xt).”