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New AI method uses supervised training for mutual information estimation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed MIST, a novel approach to estimating mutual information (MI) using supervised training with neural networks. This method leverages a large meta-dataset of synthetic distributions to train the network end-to-end, achieving performance that surpasses traditional baselines. MIST also incorporates uncertainty quantification through quantile regression and offers faster inference times compared to existing neural methods, making it a flexible and efficient tool for MI estimation. AI

IMPACT Provides a more efficient and flexible method for estimating mutual information, potentially improving various machine learning pipelines.

RANK_REASON The cluster contains an academic paper detailing a new method for mutual information estimation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AI method uses supervised training for mutual information estimation

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The cluster contains an academic paper detailing a new method for mutual information estimation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · German Gritsai, Megan Richards, Maxime M\'eloux, Kyunghyun Cho, Maxime Peyrard ·

    MIST: Mutual Information Estimation Via Supervised Training

    arXiv:2511.18945v4 Announce Type: replace Abstract: We propose a fully data-driven approach to designing mutual information (MI) estimators. Since any MI estimator is a function of the observed sample from two random variables, we parameterize this function with a neural network …

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