mlsanityData to 7 Oct 2026

Spread Divergence

arXiv 1811.08968 ↗First posted 21 Nov 2018stat.ML cs.LGcited by 23

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Abstract (arXiv)

For distributions mathbbP and mathbbQ with different supports or undefined densities, the divergence textrmD(mathbbP||mathbbQ) may not exist. We define a Spread Divergence tildetextrmD(mathbbP||mathbbQ) on modified mathbbP and mathbbQ and describe sufficient conditions for the existence of such a divergence. We demonstrate how to maximize the discriminatory power of a given divergence by parameterizing and learning the spread. We also give examples of using a Spread Divergence to train implicit generative models, including linear models (Independent Components Analysis) and non-linear models (Deep Generative Networks).

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Nov 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 7; 2023: 6; 2024: 3; 2025: 3; 2026: 1.

23 papers.

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  1. 2021Information-Theoretic Generalization Bounds for Stochastic Gradient Descentcited by 84
  2. 2022Diagnosing and Fixing Manifold Overfitting in Deep Generative Modelscited by 28
  3. 2023Free-form Flows: Make Any Architecture a Normalizing Flowcited by 26
  4. 2024Diffusive Gibbs Samplingcited by 21
  5. 2024Training Neural Samplers with Reverse Diffusive KL Divergencecited by 21
  6. 2022Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimizationcited by 18
  7. 2022Towards Healing the Blindness of Score Matchingcited by 13
  8. 2024Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matchingcited by 13
  9. 2022Generalization Gap in Amortized Inferencecited by 10
  10. 2023The Score-Difference Flow for Implicit Generative Modelingcited by 9
  11. 2023Energy Discrepancies: A Score-Independent Loss for Energy-Based Modelscited by 9
  12. 2025DiffRatio: Training One-Step Diffusion Models Without Teacher Supervisioncited by 8
11 more
  1. 2023Moment Matching Denoising Gibbs Samplingcited by 7
  2. 2022Denoising Deep Generative Modelscited by 6
  3. 2023Estimating the Rate-Distortion Function by Wasserstein Gradient Descentcited by 5
  4. 2022Out-of-Distribution Detection with Class Ratio Estimationcited by 4
  5. 2023Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancycited by 4
  6. 2021Conditional COT-GAN for Video Prediction with Kernel Smoothingcited by 3
  7. 2025On Convolutions, Intrinsic Dimension, and Diffusion Modelscited by 2
  8. 2025VarDiU: A Variational Diffusive Upper Bound for One-Step Diffusion Distillationcited by 1
  9. 2021Variational Autoencoders: A Harmonic Perspectivecited by 0
  10. 2022Integrated Weak Learningcited by 0
  11. 2026Guidance for Prior Change via Density Ratio Estimationcited by 0