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

30 Jan 2019 Casey Chu Jose Blanchet Peter Glynn

This paper provides 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 actor-critic methods in reinforcement learning can all be seen through the lens of our framework... (read more)

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