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Conditional Generative Moment-Matching Networks

arXiv 1606.04218 ↗First posted 14 Jun 2016cs.LGcited by 24

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Abstract (arXiv)

Maximum mean discrepancy (MMD) has been successfully applied to learn deep generative models for characterizing a joint distribution of variables via kernel mean embedding. In this paper, we present conditional generative moment- matching networks (CGMMN), which learn a conditional distribution given some input variables based on a conditional maximum mean discrepancy (CMMD) criterion. The learning is performed by stochastic gradient descent with the gradient calculated by back-propagation. We evaluate CGMMN on a wide range of tasks, including predictive modeling, contextual generation, and Bayesian dark knowledge, which distills knowledge from a Bayesian model by learning a relatively small CGMMN student network. Our results demonstrate competitive performance in all the tasks.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jun 2016.

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Citing papers per year: 2021: 4; 2022: 2; 2023: 5; 2024: 6; 2025: 6; 2026: 1.

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  1. 2022Calibration tests beyond classificationcited by 20
  2. 2021Composite Goodness-of-fit Tests with Kernelscited by 16
  3. 2023Market-GAN: Adding Control to Financial Market Data Generation with Semantic Contextcited by 13
  4. 2023Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networkscited by 10
  5. 2024Cauchy-Schwarz Divergence Information Bottleneck for Regressioncited by 9
  6. 2022Evaluating Aleatoric Uncertainty via Conditional Generative Modelscited by 8
  7. 2023A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Modelscited by 8
  8. 2021A deep learning based surrogate model for stochastic simulatorscited by 6
  9. 2023The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Makingcited by 6
  10. 2024Domain Adaptation with Cauchy-Schwarz Divergencecited by 6
  11. 2024A Kernel-Based Conditional Two-Sample Test Using Nearest Neighbors (with Applications to Calibration, Regression Curves, and Simulation-Based Inference)cited by 5
  12. 2024MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagationcited by 2
12 more
  1. 2024When Invariant Representation Learning Meets Label Shift: Insufficiency and Theoretical Insightscited by 2
  2. 2025All Models Are Miscalibrated, But Some Less So: Comparing Calibration with Conditional Mean Operatorscited by 2
  3. 2021Learning to Transfer with von Neumann Conditional Divergencecited by 1
  4. 2025KAIROS: Scalable Model-Agnostic Data Valuationcited by 1
  5. 2025Explainable Multimodal Regression via Information Decompositioncited by 1
  6. 2021Learning a Compressive Sensing Matrix with Structural Constraints via Maximum Mean Discrepancy Optimizationcited by 0
  7. 2023Constructing Synthetic Treatment Groups without the Mean Exchangeability Assumptioncited by 0
  8. 2024Unsupervised Domain Adaptation for Action Recognition via Self-Ensembling and Conditional Embedding Alignmentcited by 0
  9. 2025Contextual Scenario Generation for Two-Stage Stochastic Programmingcited by 0
  10. 2025Boosting Statistic Learning with Synthetic Data from Pretrained Large Modelscited by 0
  11. 2025One-shot Conditional Sampling: MMD meets Nearest Neighborscited by 0
  12. 2026Integrating Contextual Embeddings into Evaluation of Expressive MIDI Piano Performancescited by 0