#  Statistics Colloquium: Yue Lu (Harvard) 

 



####  calendar\_today Date and Time 

 **March 2, 2020** 

 12:00PM - 01:00PM EST 

####  pin\_drop Location 

 **Science Center, Hall E**  



 

 



 

###    ![Headshot of Yue Lu](/sites/g/files/omnuum10116/files/styles/hwp_1_1__360x360_scale/public/statistics-2/files/yue_lue_headshot_cropped.png?itok=Bz5bqoun) 

 

Title:

 Asymptotic Methods for High-Dimensional Estimation and Learning

###  Abstract:

 I will present recent work on using asymptotic methods from probability theory and mean-field statistical physics to understand problems in high-dimensional estimation and learning. In particular, I will show (1) the exact characterization of a spectral method widely used in effective dimension reduction and exploratory data analysis; (2) the fundamental limits of solving the phase retrieval problem via linear programming; and (3) how to use scaling and mean-field limits to analyze iterative algorithms for nonconvex optimization. In all these problems, asymptotic methods clarify some of the fascinating phenomena, such as phase transitions, that emerge with high-dimensional data. They also lead to optimal designs that significantly outperform heuristic choices commonly used in practice.



 

 



 

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