Casey Chu — accepted AI-conference papers

2020

Smoothness and Stability in GANs

Casey Chu, Kentaro Minami, Kenji Fukumizu

ICLR 2020poster

Generative adversarial networks, or GANs, commonly display unstable behavior during training. In this work, we develop a principled theoretical framework for understanding the stability of various types of GANs. In particular, we derive conditions that guarantee eventual stationarity of the generato…

Cited by 79 Source ↗Scholar ↗

2019

Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning

Casey Chu, Jose Blanchet, Peter Glynn

ICML 2019oral

The goal of this paper is to provide a unifying view of a wide range of problems of interest in machine learning by framing them as the minimization of functionals defined on the space of probability measures. In particular, we show that generative adversarial networks, variational inference, and ac…

Cited by 36 Source ↗PDF ↗Scholar ↗