mlsanityData to 7 Oct 2026

Information Theoretical Estimators Toolbox

arXiv 1405.2106 ↗First posted 8 May 2014cs.IT math.IT math.STcited by 15

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Abstract (arXiv)

We present ITE (information theoretical estimators) a free and open source, multi-platform, Matlab/Octave toolbox that is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities, and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems. ITE also includes a prototype application in a central problem class of signal processing, independent subspace analysis and its extensions.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 May 2014.

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Citing papers per year: 2021: 4; 2022: 0; 2023: 3; 2024: 7; 2025: 0; 2026: 1.

15 papers.

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  1. 2024Provable Benefit of Annealed Langevin Monte Carlo for Non-log-concave Samplingcited by 23
  2. 2023Semi-Implicit Variational Inference via Score Matchingcited by 19
  3. 2023Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Accelerationcited by 14
  4. 2024Reflected Flow Matchingcited by 13
  5. 2024Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Modelscited by 12
  6. 2024Kernel Semi-Implicit Variational Inferencecited by 8
  7. 2021Efficient Multi-Modal Embeddings from Structured Datacited by 3
  8. 2024Information-Theoretic Measures on Lattices for High-Order Interactionscited by 3
  9. 2023Picking Up Quantization Steps for Compressed Image Classificationcited by 2
  10. 2024Learning Representations for Independence Testingcited by 2
  11. 2021Enabling Inference Privacy with Adaptive Noise Injectioncited by 1
  12. 2021Causal Identification with Additive Noise Models: Quantifying the Effect of Noisecited by 1
3 more
  1. 2024Bayesian Adaptive Calibration and Optimal Designcited by 1
  2. 2026A Kernel Approach for Semi-implicit Variational Inferencecited by 1
  3. 2021The Effect of Noise Level on Causal Identification with Additive Noise Modelscited by 0