This event is part of Distributed Solutions to Complex Societal Problems View Details

Short Courses on the Mean Field Approach in Machine Learning and Statistics

Description

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This online workshop will consist of three series of lectures discussing aspects of the Mean Field approach in machine learning and statistics.

Speakers

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Q L
Qianxiao Li National University of Singapore
S M
Song Mei University of California, Berkeley
G R
Grant Rotskoff Stanford University

Schedule

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Monday, October 18, 2021
12:00-13:00 CDT
Mean Field Asymptotics for High Dimensional Linear Models (Part 1)

Online only

Speaker: Song Mei (University of California, Berkeley)

13:30-14:30 CDT
Deep Learning, Dynamical Systems and Optimal Control (Part 1)

Online only

Speaker: Qianxiao Li (National University of Singapore)

15:00-16:00 CDT
Mean Field Asymptotics for High Dimensional Linear Models (Part 2)

Online only

Speaker: Song Mei (University of California, Berkeley)

Tuesday, October 19, 2021
9:30-10:30 CDT
Deep Learning, Dynamical Systems and Optimal Control (Part 2)

Online only

Speaker: Qianxiao Li (National University of Singapore)

11:00-12:00 CDT
The Mean-Field Limit for Shallow Neural Networks: Implications for Trainability and Generalization (Part 1)

Online only

Speaker: Grant Rotskoff (Stanford University)

Wednesday, October 20, 2021
10:30-11:30 CDT
Deep Learning, Dynamical Systems and Optimal Control (Part 3)

Online only

Speaker: Qianxiao Li (National University of Singapore)

12:00-13:00 CDT
The Mean-Field Limit for Shallow Neural Networks: Implications for Trainability and Generalization (Part 2)

Online only

Speaker: Grant Rotskoff (Stanford University)

13:30-14:30 CDT
Mean Field Asymptotics for High Dimensional Linear Models (Part 3)

Online only

Speaker: Song Mei (University of California, Berkeley)


Registration

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