TU2.R1: Bayesian estimation
Tue, 9 Jul, 11:30 - 12:50
Location: Ballroom II & III
Session Chair: Wojtek Szpankowski, Purdue Univeristy
Track: 8: Learning Theory
Tue, 9 Jul, 11:30 - 11:50

TU2.R1.1: Low Complexity Approximate Bayesian Logistic Regression for Sparse Online Learning
Gil I. Shamir, Google, United States; Wojciech Szpankowski, Purdue University, United States
Tue, 9 Jul, 11:50 - 12:10

TU2.R1.2: Personalized heterogeneous Gaussian mean estimation under communication constraints
Ruida Zhou, Suhas Diggavi, University of California Los Angeles, United States
Tue, 9 Jul, 12:10 - 12:30

TU2.R1.3: Multivariate Priors and the Linearity of Optimal Bayesian Estimators under Gaussian Noise
Leighton Barnes, Center for Communications Research, United States; Alex Dytso, Qualcomm, United States; Jingbo Liu, University of Illinois, United States; H Vincent Poor, Princeton University, United States
Tue, 9 Jul, 12:30 - 12:50

TU2.R1.4: Bayesian Persuasion: From Persuasion toward Counter-suasion
Ananya Das, IIT Kharagpur, India; Aishwarya Soni, Independent researcher, India; Amitalok Budkuley, IIT Kharagpur, India