We study the problem of contextual feature selection, where the goal is to
learn a predictive function while identifying subsets of informative features
conditioned on specific contexts. Towards this goal, we generalize the recently
proposed stochastic gates (STG) Yamada et al. [2020] by modeling the
probabilistic gates as conditional Bernoulli variables whose parameters are
predicted based on the contextual variables. Our new scheme, termed
conditional-STG (c-STG), comprises two networks: a hypernetwork that
establishes the mapping between contextual variables and probabilistic feature
selection parameters and a prediction network that maps the selected feature to
the response variable. Training the two networks simultaneously ensures the
comprehensive incorporation of context and feature selection within a unified
model. We provide a theoretical analysis to examine several properties of the
proposed framework. Importantly, our model leads to improved flexibility and
adaptability of feature selection and, therefore, can better capture the
nuances and variations in the data. We apply c-STG to simulated and real-world
datasets, including healthcare, housing, and neuroscience, and demonstrate that
it effectively selects contextually meaningful features, thereby enhancing
predictive performance and interpretability.

我们研究了上下文特征选择的问题，提出了一种新的条件性 STG 模型，通过建立超网络和预测网络的联合模型，在学习预测函数的同时，根据特定背景和上下文变量选择最具信息量的特征，实现了更好的预测性能和解释性。