MO4.R2.3

Theoretical Guarantees of Data Augmented Last Layer Retraining Methods

Monica Welfert, Nathan Stromberg, Lalitha Sankar, Arizona State University, United States
Session:
Topics in Machine Learning 2
Track:
8: Machine Learning
Location:
Ypsilon I-II-III
Presentation Time:
Mon, 8 Jul, 17:05 - 17:25
Session Chair:
Lalitha Sankar, Arizona State University
Presentation
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Discussion
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Session MO4.R2
MO4.R2.1: Conditional Mutual Information Constrained Deep Learning: Framework and Preliminary Results
En-Hui Yang, Shayan Mohajer Hamidi, Linfeng Ye, Renhao Tan, University of Waterloo, Canada; Beverly Yang, University of British Columbia, Canada
MO4.R2.2: Supervised Contrastive Representation Learning: Landscape Analysis with Unconstrained Features
Tina Behnia, Christos Thrampoulidis, University of British Columbia, Canada
MO4.R2.3: Theoretical Guarantees of Data Augmented Last Layer Retraining Methods
Monica Welfert, Nathan Stromberg, Lalitha Sankar, Arizona State University, United States
MO4.R2.4: Robust VAEs via Generating Process of Noise Augmented Data
Hiroo Irobe, Wataru Aoki, Tokyo Institute of Technology, Japan; Kimihiro Yamazaki, Fujitsu, Japan; Yuhui Zhang, Tokyo Institute of Technology, Japan; Takumi Nakagawa, Tokyo Institute of Technology, RIKEN AIP, Japan; Hiroki Waida, Tokyo Institute of Technology, Japan; Yuichiro Wada, Fujitsu, RIKEN AIP, Japan; Takafumi Kanamori, Tokyo Institute of Technology, RIKEN AIP, Japan
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