Conditional Density Estimation via Least-Squares Density Ratio Estimation

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Kernel Belief Propagation (2011, methodology): “Therefore once again, edge potentials were selected using the technique described in Wainwright et al. (2003).”
Kernel Bayes' Rule (2010, methodology): “Some representative applications of the kernel Bayes’ rule are presented, including Baysian computation without likelihood and filtering with a nonparametric state-space model.”
Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation (2017, methodology): “…to conditional density estimation in low dimensions include using locally polynomial regression (Fan et al., 1996), least squares approaches (Sugiyama et al., 2010) and density estimation through quantile estimation (Takeuchi et al., 2009); see Bertin et al. (2016) and references therein…”
Kernel Exponential Family Estimation via Doubly Dual Embedding (2018, methodology): “In this part of the experiment, models are trained to estimate the conditional distribution p (y|x) on the benchmark datasets for studying these methods (Arbel and Gretton, 2017; Sugiyama et al., 2010).”