Learning structured densities via infinite dimensional exponential families

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Density Estimation in Infinite Dimensional Exponential Families (2013, methodology): “, 2015) and graphical model structure learning (Sun et al., 2015) where the superiority of working with the infinite dimensional family is demonstrated.”
Structure Learning in Graphical Modeling (2016, background): “The resulting estimators of conditional independence graphs are studied by Lin et al. (2016) who also treat nonnegative observations, by Janofsky (2015) who proposes a nonparametric exponential series approach, and by Sun et al. (2015) who consider inﬁnite-dimensional exponential families.”
Learning deep kernels for exponential family densities (2018, methodology): “All previous applications of score matching in the kernel exponential family of which we are aware (e.g. Sriperumbudur et al., 2017; Strathmann et al., 2015; Sun et al., 2015; Sutherland et al., 2018) have used kernels of the form k(x,y) = exp ( − 1 2σ2 ‖x− y‖2 ) +r ( xy + c )2 , with kernel paramet”