Mathematical Statistics: Basic Ideas and Selected Topics, Volume II

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Metalearners for estimating heterogeneous treatment effects using machine learning (2017, background): “Let us start by reviewing some of the basic results in the field of minimax nonparametric regression estimation: Bretagnolle and Huber (1979); Stone (1982); Birgé (1983); Bickel et al. (1998); Korostelev and Tsybakov (2012), also see textbooks Györfi et al.”
Monte Carlo Gradient Estimation in Machine Learning (2019, methodology): “This case study makes use of the delta method (Bickel and Doksum, 2015), which is a method for describing the asymptotic variance of a function of a random variable.”
A General Method for Robust Bayesian Modeling (2015, methodology): “Here we use the multivariate delta method to approximate the objective (Bickel and Doksum, 2007; Wang and Blei, 2013).”
Variational inference in nonconjugate models (2012, unknown): “The objective is (cid:8) (cid:9) This is the multivariate delta method for evaluating E q ( θ ) [ f ( θ )] (Bickel and Doksum, 2007).”
Bayesian Differential Privacy through Posterior Sampling (2013, background): “theory (Berger, 1985; Bickel and Doksum, 2001; DeGroot, 1970), learning is cast as a statistical inference problem and decision-theoretic criteria are used as a basis for assessing, selecting and designing procedures.”