Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/137686
Title: DATA-EFFICIENT MACHINE LEARNING WITH MULTIPLE OUTPUT TYPES AND HIGH INPUT DIMENSIONS
Authors: ZHANG YEHONG
Keywords: Machine learning; Data-efficient machine learning; Active learning; Bayesian optimization; Multi-output learning; High-dimensional input
Issue Date: 25-Aug-2017
Citation: ZHANG YEHONG (2017-08-25). DATA-EFFICIENT MACHINE LEARNING WITH MULTIPLE OUTPUT TYPES AND HIGH INPUT DIMENSIONS. ScholarBank@NUS Repository.
Abstract: Recent research works in machine learning (ML) have focused on learning some target variables of interest to achieve competitive (or state-of-the-art) predictive performance in less time but without requiring large quantities of data, which is known as data-efficient ML. This thesis focuses on two highly related data-efficient ML approaches: active learning (AL) and Bayesian optimization (BO) which, instead of learning passively from a given set of data, need to select and gather the most informative observations for learning the target variables of interest accurately given some budget constraints. In particular, this thesis aims to (a) exploit the auxiliary types of outputs which correlate with the target variables for improving the learning performance of the target output type in both AL and BO algorithms and (b) scale up the state-of-the-art BO algorithm to high input dimensions.
URI: http://scholarbank.nus.edu.sg/handle/10635/137686
Appears in Collections:Ph.D Theses (Open)

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