In our previous paper on benchmarking #MutualInformation estimators, we focus on going "beyond normal" distributions. However, it turns out that we can do much more, as we show in our new TMLR paper: "On the Properties and Estimation of Pointwise Mutual Information Profiles" 1/2

Whether you're analyzing data or optimizing models, understanding how information is distributed and shared can give you a serious edge.

#InformationTheory #MachineLearning #Entropy #MutualInformation #DataScience #AI #ML #DataAnalysis

MINERVA, a feature‑selection method, was tested on synthetic benchmarks and fraud detection data, achieving accuracy with fewer features than traditional filters. https://getnews.me/minerva-introduces-neural-mutual-information-for-feature-selection/ #minerva #mutualinformation

A protocol merges classical and neural MI estimators, adds bootstrap confidence intervals and uses probabilistic critics for stable high‑dimensional data. Read more: https://getnews.me/reliable-mutual-information-estimation-for-high-dimensional-data/ #mutualinformation #highdimensional

InfoBridge uses diffusion‑bridge models to estimate mutual information on high‑dimensional data as images and protein embeddings. The paper was posted September 2025. Read more: https://getnews.me/infobridge-diffusion-bridge-method-for-mutual-information-estimation/ #infobridge #mutualinformation

Researchers used mutual information to evaluate latent variables in generators, showing synthetic views match or exceed real‑image performance in contrastive learning. Code on GitHub. https://getnews.me/mutual-information-boosts-multi-latent-image-generators/ #mutualinformation #syntheticdata