Adversarial Learning of a Sampler Based on an Unnormalized Distribution
Publication
, Conference
Li, C; Bai, K; Li, J; Wang, G; Chen, C; Carin, L
Published in: Proceedings of Machine Learning Research
January 1, 2019
We investigate adversarial learning in the case when only an unnormalized form of the density can be accessed, rather than samples. With insights so garnered, adversarial learning is extended to the case for which one has access to an unnormalized form u(x) of the target density function, but no samples. Further, new concepts in GAN regularization are developed, based on learning from samples or from u(x). The proposed method is compared to alternative approaches, with encouraging results demonstrated across a range of pplications, including deep soft Q-learning.
Duke Scholars
Published In
Proceedings of Machine Learning Research
EISSN
2640-3498
Publication Date
January 1, 2019
Volume
89
Start / End Page
3302 / 3311
Citation
APA
Chicago
ICMJE
MLA
NLM
Li, C., Bai, K., Li, J., Wang, G., Chen, C., & Carin, L. (2019). Adversarial Learning of a Sampler Based on an Unnormalized Distribution. In Proceedings of Machine Learning Research (Vol. 89, pp. 3302–3311).
Li, C., K. Bai, J. Li, G. Wang, C. Chen, and L. Carin. “Adversarial Learning of a Sampler Based on an Unnormalized Distribution.” In Proceedings of Machine Learning Research, 89:3302–11, 2019.
Li C, Bai K, Li J, Wang G, Chen C, Carin L. Adversarial Learning of a Sampler Based on an Unnormalized Distribution. In: Proceedings of Machine Learning Research. 2019. p. 3302–11.
Li, C., et al. “Adversarial Learning of a Sampler Based on an Unnormalized Distribution.” Proceedings of Machine Learning Research, vol. 89, 2019, pp. 3302–11.
Li C, Bai K, Li J, Wang G, Chen C, Carin L. Adversarial Learning of a Sampler Based on an Unnormalized Distribution. Proceedings of Machine Learning Research. 2019. p. 3302–3311.
Published In
Proceedings of Machine Learning Research
EISSN
2640-3498
Publication Date
January 1, 2019
Volume
89
Start / End Page
3302 / 3311