2015/03/11 by Shahin Shahrampour, Mohammad Amin Rahimian, M. Amin Rahimian +4
Computer Science · Decision Sciences · Mathematics · #Advanced Bandit Algorithms Research #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #cs.LG #math.OC #stat.ML
paper · pdf · doi:10.48550/arxiv.1503.03517
6 pages, To appear in American Control Conference 2015
arxiv created 2015/03/11 · openalex publication_date 2015/03/11 · arxiv updated 2015/03/13 · openalex created_date 2024/04/10 · openalex updated_date 2026/07/28
A network of agents attempt to learn some unknown state of the world drawn by nature from a finite set. Agents observe private signals conditioned on the true state, and form beliefs about the unknown state accordingly. Each agent may face an identification problem in the sense that she cannot distinguish the truth in isolation. However, by communicating with each other, agents are able to benefit from side observations to learn the truth collectively. Unlike many distributed algorithms which rely on all-time communication protocols, we propose an efficient method by switching between Bayesian and non-Bayesian regimes. In this model, agents exchange information only when their private signals are not informative enough; thence, by switching between the two regimes, agents efficiently learn the truth using only a few rounds of communications. The proposed algorithm preserves learnability while incurring a lower communication cost. We also verify our theoretical findings by simulation examples.