March 22, 2024, 4:43 a.m. | Fengjiao Li, Xingyu Zhou, Bo Ji

cs.LG updates on arXiv.org arxiv.org

arXiv:2207.05827v2 Announce Type: replace
Abstract: In this paper, we study the problem of global reward maximization with only partial distributed feedback. This problem is motivated by several real-world applications (e.g., cellular network configuration, dynamic pricing, and policy selection) where an action taken by a central entity influences a large population that contributes to the global reward. However, collecting such reward feedback from the entire population not only incurs a prohibitively high cost but often leads to privacy concerns. To tackle …

abstract applications arxiv cellular cs.cr cs.lg cs.na distributed dynamic dynamic pricing feedback global linear math.na network paper policy population pricing study type world

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

MLOps Engineer - Hybrid Intelligence

@ Capgemini | Madrid, M, ES

Analista de Business Intelligence (Industry Insights)

@ NielsenIQ | Cotia, Brazil