On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results

By Tengyuan Liang. 2018

47 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jie Gui
2. Dacheng Tao
3. Zhenan Sun
4. Yonggang Wen
5. Jieping Ye
6. Yu Zhang
7. Shuicheng Yan
8. Qiang Yang

Citations in the corpus

• Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands (2018, methodology): “Deep nets, in the formulation of GANs, are a promising tool for distribution estimation (Liang (2018), Athey, Imbens, Metzger, and Munro (2019)).”
• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications (2020, background): “[308] studied how well GANs learn densities, including nonparametric and parametric target distributions.”