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Contributed Papers

New Developments in Machine and Deep Learning

Sun, Aug 4, 2:00 PM - 3:50 PM

Reetam MajumderChair

IMS

7 Presentations

A Larger Stepsize Improves Gradient Descent in Classisificaion Problems

2:05 PM - 2:20 PM

Co-authors: Matus Telgarsky (New York University), Bin Yu (University of California at Berkeley), Peter Bartlett (University of California Berkeley)

CONDITION INDEPENDENCE WITH DEEP NEURAL NETWORK BASED BINARY EXPANSION TEST (DEEPBET)

2:20 PM - 2:35 PM

Co-authors: Kai Zhang (UNC Chapel Hill), Ping-Shou Zhong (University of Illinois at Chicago)

Deep Fréchet Regression

2:35 PM - 2:50 PM

Co-authors: Yidong Zhou, Hans-Georg Mueller (UC Davis)

Inference via Machine Learning

2:50 PM - 3:05 PM

Tijana Zrnic (University of California)

Learning time-scales in two-layers neural networks

3:05 PM - 3:20 PM

Kangjie Zhou (Stanford University)

Simultaneous Classification and Feature Selection for Complex Functional Data

3:20 PM - 3:35 PM

guanqun Cao (Michigan State University)

Co-authors: Shuoyang Wang (Yale University)

Trade-off between dependence and complexity in Wasserstein distance learning

3:35 PM - 3:50 PM

Co-authors: Young-Heon Kim (University of British Columbia), Soumik Pal (University of Washington, Seattle), Geoffrey Schiebinger (University of British Columbia)

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