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September 2019

How Well Generative Adversarial Networks Learn Distributions (and Beyond)?

Tengyuan Liang (University of Chicago)

September 6 @ 11:00 am - 12:00 pm
E18-304

Abstract: We study in this paper the rate of convergence for learning distributions with the adversarial framework and Generative Adversarial Networks (GANs), which subsumes Wasserstein, Sobolev and MMD GANs as special cases. We study a wide range of parametric and nonparametric target distributions, under a collection of objective evaluation metrics. On the nonparametric end, we investigate the minimax optimal rates and fundamental difficulty of the density estimation under the adversarial framework. On the parametric end, we establish a theory for…

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Data Science and Big Data Analytics: Making Data-Driven Decisions

September 30
online

The seven-week course launches September 30, 2019. This course was developed by over ten MIT faculty members at IDSS. It is specially designed for data scientists, business analysts, engineers, and technical managers looking to learn the latest theories and strategies to harness data.

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