Model-augmented conditional mutual information estimation for feature selection

Alan Yang, Amir Emad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum

Research output: Contribution to conferencePaperpeer-review


Markov blanket feature selection, while theoretically optimal, is generally challenging to implement. This is due to the shortcomings of existing approaches to conditional independence (CI) testing, which tend to struggle either with the curse of dimensionality or computational complexity. We propose a novel two-step approach which facilitates Markov blanket feature selection in high dimensions. First, neural networks are used to map features to low-dimensional representations. In the second step, CI testing is performed by applying the k-NN conditional mutual information estimator to the learned feature maps. The mappings are designed to ensure that mapped samples both preserve information and share similar information about the target variable if and only if they are close in Euclidean distance. We show that these properties boost the performance of the k-NN estimator in the second step. The performance of the proposed method is evaluated on both synthetic and real data.

Original languageEnglish (US)
Number of pages10
StatePublished - 2020
Externally publishedYes
Event36th Conference on Uncertainty in Artificial Intelligence, UAI 2020 - Virtual, Online
Duration: Aug 3 2020Aug 6 2020


Conference36th Conference on Uncertainty in Artificial Intelligence, UAI 2020
CityVirtual, Online

ASJC Scopus subject areas

  • Artificial Intelligence


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