Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/132189
Title: EXPLOITING DECENTRALIZED MULTI-AGENT COORDINATION FOR LARGE-SCALE MACHINE LEARNING PROBLEMS
Authors: OUYANG RUOFEI
Keywords: Multi-Agent System, Machine Learning, Gaussian process, Bayesian Optimization
Issue Date: 29-Jul-2016
Citation: OUYANG RUOFEI (2016-07-29). EXPLOITING DECENTRALIZED MULTI-AGENT COORDINATION FOR LARGE-SCALE MACHINE LEARNING PROBLEMS. ScholarBank@NUS Repository.
Abstract: Nowadays, the scale of machine learning problems becomes much larger than before. It raises a huge demand in distributed perception and distributed computation. A multi-agent system provides exceptional scalability for problems like active sensing and data fusion. However, many rich characteristics of large-scale machine learning problems have not been addressed yet such as large input domain, nonstationarity, and high dimensionality. This thesis identifes the challenges related to these characteristics from multi-agent perspective. By exploiting the correlation structure of data in large-scale problems, we propose multiagent coordination schemes that can improve the scalability of the machine learning models while preserving the computation accuracy.
URI: http://scholarbank.nus.edu.sg/handle/10635/132189
Appears in Collections:Ph.D Theses (Open)

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