Research map / Probabilistic and scientific modeling / Variational and generative sampling
Beyond Normal: On the Evaluation of Mutual Information Estimators
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase sparsity of interactions (2-pair vs dense) for KSG | KSG · Multinormal 5x5 to 25x25 | MI estimate accuracy: 70% -> 10% of true MI | Hurt |
| use neural estimators instead of KSG under sparse interactions | neural MI estimators · sparse multinormal | MI estimate accuracy | Helped |
| lengthen tails via |x|^k sgn x with larger k | CCA, KSG, neural estimators · multivariate normal | MI estimate accuracy | Hurt |
| apply tail-shortening asinh transform to Student distributions | MI estimators · multivariate Student | MI estimate accuracy | Helped |
| apply spiral diffeomorphism with stronger speed v | all estimators · multinormal 2D X, 1D Y | MI estimate accuracy | Hurt |
| apply spiral diffeomorphism at high MI | neural estimators · 3x3 multinormal | MI estimate accuracy: accurate up to 5 nats -> inaccurate at 2 nats | Hurt |
| use CCA model-based estimator on slightly transformed normals | CCA · multivariate normal transformed | MI estimate accuracy | Helped |
Closest papers by meaning
- A Benchmark Suite for Evaluating Neural Mutual Information Estimators on Unstructured Datasets (NeurIPS 2024 · similarity 0.91)
- InfoNet: Neural Estimation of Mutual Information without Test-Time Optimization (ICML 2024 · similarity 0.90)
- Mutual Information Estimation via $f$-Divergence and Data Derangements (NeurIPS 2024 · similarity 0.89)
- Neural Mutual Information Estimation with Vector Copulas (NeurIPS 2025 · similarity 0.89)
- MINDE: Mutual Information Neural Diffusion Estimation (ICLR 2024 · similarity 0.88)
- Mutual Information Estimation via Normalizing Flows (NeurIPS 2024 · similarity 0.87)
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