Supervised Neighborhoods for Distributed Nonparametric Regression

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Local Linear Forests (2018, methodology): “As mentioned in the introduction, a predecessor to this work is a paper by Bloniarz et al. (2016), who consider local linear regression with supervised weighting functions, including ones produced by a forest.”
Model Agnostic Supervised Local Explanations (2018, methodology): “Under certain regularity conditions, SILO has been shown to be consistent in that its estimator converges in probability to the true function [3].”
RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling (2019, methodology): “Supervised Local modeling methods (SILO) (Bloniarz et al., 2016)) aims to improve LIME by determining the neighborhoods for each instance using ad-hoc tree-based ensemble meth-ods.”
Regularizing Black-box Models for Improved Interpretability (2019, methodology): “In this ﬁnal experiment, we ﬁt a convolutional neural network to MNIST and then evaluate the stability of its 6 The task is to predict release year of song from a set of acoustic features, treated as a regression problem as in Bloniarz et al. (2016); the dataset is denoted MSD in Table 2.”