|Seminar on Theoretical Machine Learning|
|Topic:||Some Statistical Results on Deep Learning: Interpolation, Optimality and Sparsity|
|Affiliation:||Purdue University; Member, School of Mathematics|
|Date:||Wednesday, November 13|
|Time/Room:||12:00pm - 1:30pm/Dilworth Room|
This talk discusses three aspects of deep learning from a statistical perspective: interpolation, optimality and sparsity. The first one attempts to interpret the double descent phenomenon by precisely characterizing a U-shaped curve within the “over-fitting regime,” while the second one focuses on the statistical optimality of neural network classification in a student-teacher framework. This talk is concluded by proposing sparsity induced training of neural network with statistical guarantee.