  Estimation of information theoretic quantities such as mutual information and
its conditional variant has drawn interest in recent times owing to their
multifaceted applications. Newly proposed neural estimators for these
quantities have overcome severe drawbacks of classical $k$NN-based estimators
in high dimensions. In this work, we focus on conditional mutual information
(CMI) estimation by utilizing its formulation as a minmax optimization problem.
Such a formulation leads to a joint training procedure similar to that of
generative adversarial networks. We find that our proposed estimator provides
better estimates than the existing approaches on a variety of simulated data
sets comprising linear and non-linear relations between variables. As an
application of CMI estimation, we deploy our estimator for conditional
independence (CI) testing on real data and obtain better results than
state-of-the-art CI testers.
