Bayesian Nonparametric Learning Using the Maximum Mean Discrepancy Measure for Synthetic Data Generation

Abstract

We introduce a Bayesian estimator for maximum mean discrepancy (MMD), enabling a novel approach to measure-based data generation. To demonstrate the adaptability of our method, we embed this estimator within a generative adversarial network (GAN) framework. This integration offers a powerful avenue for Bayesian nonparametric (BNP) learning, showcasing the estimator's broad applicability. Our BNP-driven GAN not only enhances sample diversity but also improves inferential accuracy, surpassing the performance of traditional methods. Further theoretical properties, proofs, and experiments are given by the Appendix.

PDF NeurIPSW OpenReview Semantic Scholar

Cite

Text

Fazeli-Asl et al. "Bayesian Nonparametric Learning Using the Maximum Mean Discrepancy Measure for Synthetic Data Generation." NeurIPS 2024 Workshops: BDU, 2024.

Markdown

[Fazeli-Asl et al. "Bayesian Nonparametric Learning Using the Maximum Mean Discrepancy Measure for Synthetic Data Generation." NeurIPS 2024 Workshops: BDU, 2024.](https://mlanthology.org/neuripsw/2024/fazeliasl2024neuripsw-bayesian/)

BibTeX

@inproceedings{fazeliasl2024neuripsw-bayesian,
  title     = {{Bayesian Nonparametric Learning Using the Maximum Mean Discrepancy Measure for Synthetic Data Generation}},
  author    = {Fazeli-Asl, Forough and Zhang, Michael Minyi and Lin, Lizhen},
  booktitle = {NeurIPS 2024 Workshops: BDU},
  year      = {2024},
  url       = {https://mlanthology.org/neuripsw/2024/fazeliasl2024neuripsw-bayesian/}
}