MO3.R2: Classification and Regression
Mon, 8 Jul, 14:35 - 15:55
Location: Ypsilon I-II-III
Session Chair: Adam Krzyzak, Concordia University
Track: 8: Machine Learning
Mon, 8 Jul, 14:35 - 14:55

MO3.R2.1: Effect of Weight Quantization on Learning Models by Typical Case Analysis
Shuhei Kashiwamura, The University of Tokyo, Japan; Ayaka Sakata, The Institute of Statistical Mathematics, Japan; Masaaki Imaizumi, The University of Tokyo, Japan
Mon, 8 Jul, 14:55 - 15:15

MO3.R2.2: Sharp information-theoretic thresholds for shuffled linear regression
Leon Lufkin, Yihong Wu, Yale University, United States; Jiaming Xu, Duke University, United States
Mon, 8 Jul, 15:15 - 15:35

MO3.R2.3: Data-Driven Estimation of the False Positive Rate of the Bayes Binary Classifier via Soft Labels
Minoh Jeong, Martina Cardone, University of Minnesota, United States; Alex Dytso, Qualcomm Flarion Technology, Inc., United States
Mon, 8 Jul, 15:35 - 15:55

MO3.R2.4: Rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent
Michael Kohler, Technical University of Darmstadt, Germany; Adam Krzyzak, Concordia University, Canada; Benjamin Walter, Technical University of Darmstadt, Germany