Bridging High-Dimensional Robust Statistics and Non-Convex Optimization
Abstract: In this talk, we will explore the intersection between robust high-dimensional statistics and non-convex optimization. We will show that standard optimization methods such as gradient descent can efficiently solve various robust estimation tasks, and conversely, robust estimation algorithms can be used to develop robust algorithms for various tractable non-convex problems. Our results could lead to more practical and provably robust algorithms for many statistical and machine learning tasks, and shed light on the broader connections between robust estimation and non-convex optimization.
This talk is based on joint work with Ilias Diakonikolas, Jelena Diakonikolas, Haichen Dong, Rong Ge, Shivam Gupta, Daniel Kane, Shuyao Li, Alessio Mazzetto, Mahdi Soltanolkotabi, and Stephen Wright.
Short Bio: Yu Cheng is an Assistant Professor in the Department of Computer Science at Brown University. He received his Ph.D. in Computer Science from the University of Southern California. Before joining Brown University, he was a postdoc at Duke University, a visiting member at the Institute for Advanced Study, and an Assistant Professor at the University of Illinois at Chicago. His main research interests include machine learning, optimization, and game theory.
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