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Abstract: Robust learning under Huber's contamination model has become an important topic in statistics and theoretical computer science. Statistically optimal procedures such as Tukey's median and other estimators based on depth functions are impractical because of their computational intractability. In this talk, we present an intriguing connection between f-GANs and various depth functions through the lens of f-Learning. Similar to the derivation of f-GANs, we show that these depth functions that lead to statistically optimal robust estimators can all be viewed as variational lower bounds of the total variation distance in the framework of f-Learning. This connection opens the door of computing robust estimators using tools developed for training GANs. In particular, we show in both theory and experiments that some appropriate structures of discriminator networks with hidden layers in GANs lead to statistically optimal robust location estimators for both Gaussian distribution and general elliptical distributions where first moment may not exist. This is a joint work with Chao Gao, Jiyi Liu, and Weizhi Zhu.
Short Bio: Yuan Yao is currently a Professor of Mathematics in Hong Kong University of Science and Technology (HKUST). Dr. Yao received his Ph.D. in Mathematics from UC Berkeley with Professor Steve Smale and worked in Stanford University and Peking University before joining HKUST in 2016. His main research interests lie in mathematics of data science and machine learning, with applications in computational biology and information technology.