Hugging Face Trending Papers
Non-Parametric Mutual Information Estimation with Vine Copulas
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arXiv Machine Learning
22h ago
Non-Parametric Mutual Information Estimation with Vine Copulas
arXiv:2611.07693v1 Announce Type: new Abstract: We introduce VINE: a non-parametric mutual information (MI) estimator based on simplified D-vine copulas. Approximating the joint density with a simpli...
arXiv AI
Jul 7
Towards Diverse and Comprehensive Benchmarks for Mutual Information Estimation
arXiv:2607. 03487v1 Announce Type: cross Abstract: Mutual information (MI) estimation is a central problem in machine learning and statistics; however, existing benchmarks typically evaluate estimators on simplified, low-dimensional distributions, leaving their performance on complex, realistic data largely unexplored.
arXiv Machine Learning
Sep 30
ALICE: In-context, Zero-shot, Mutual Information Estimation
ALICE is a foundation model that estimates mutual information (MI) without per‑distribution training. Trained only on synthetic distributions, it acts as an in‑context estimator of rectified‑flow velocity fields, producing MI via a fixed identity that integrates squared differences between joint and conditional fields. The authors validate ALICE on a challenging benchmark and demonstrate its applicability to unseen data in biology, genetics, and neuroscience, achieving performance comparable to neural estimators trained separately for each distribution.
Hugging Face Trending Papers
Jul 27
Localized Anomaly Detection via Differentiable D-vine Copulas
Vine copulas provide a flexible framework for modeling complex multivariate distributions through a hierarchical decomposition into bivariate pair-copulas. Fitting a D-vine requires selecting a copula family and parameter configuration for each pair-copula from a set of candidates encoding different dependence patterns.
arXiv Machine Learning
Jul 30
Breaking the Curse with BAND: Nonparametric Distribution Estimation in High Dimensions
arXiv:2607. 26955v1 Announce Type: cross Abstract: Minimax-optimal rates for multivariate distribution estimation are known to suffer from the curse of dimensionality.
arXiv AI
Jul 29
Localized Anomaly Detection via Differentiable D-vine Copulas
arXiv:2607. 25020v1 Announce Type: new Abstract: Vine copulas provide a flexible framework for modeling complex multivariate distributions through a hierarchical decomposition into bivariate pair-copulas.