# Conditional Mutual Information Neural Estimator

arXiv: https://arxiv.org/abs/1911.02277 · HTML: https://mlsanity.com/paper/1911.02277/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2019.

## Built on by (most cited first, 7 of 7)

- [Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities](https://mlsanity.com/paper/2602.05073/) (2026)
- [Towards quantifying information flows: relative entropy in deep neural networks and the renormalization group](https://mlsanity.com/paper/2107.06898/) (2021)
- [Disentanglement and Generalization Under Correlation Shifts](https://mlsanity.com/paper/2112.14754/) (2021)
- [An Information Theoretic Approach to Interaction-Grounded Learning](https://mlsanity.com/paper/2401.05015/) (2024)
- [Reinforce Security: A Model-Free Approach Towards Secure Wiretap Coding](https://mlsanity.com/paper/2106.00343/) (2021)
- [A Neural Network Algorithm for KL Divergence Estimation with Quantitative Error Bounds](https://mlsanity.com/paper/2510.05386/) (2025)
- [From Generalist to Specialist Representation](https://mlsanity.com/paper/2605.12733/) (2026)

## Abstract (arXiv)

Several recent works in communication systems have proposed to leverage the power of neural networks in the design of encoders and decoders. In this approach, these blocks can be tailored to maximize the transmission rate based on aggregated samples from the channel. Motivated by the fact that, in many communication schemes, the achievable transmission rate is determined by a conditional mutual information term, this paper focuses on neural-based estimators for this information-theoretic quantity. Our results are based on variational bounds for the KL-divergence and, in contrast to some previous works, we provide a mathematically rigorous lower bound. However, additional challenges with respect to the unconditional mutual information emerge due to the presence of a conditional density function which we address here.