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    Spatio-temporal change-point detection and constrained Bayesian optimization

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    CHEN-DISSERTATION-2019.pdf (2.802Mb)
    Date
    2019-04-02
    Author
    Chen, Junzhuo
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    Abstract
    This thesis makes contributions to two research topics: spatio-temporal change-point detection and constrained Bayesian optimization. Spatio-temporal change-point detection is concerned with detecting statistical anomalies based on multiple data streams collected at different locations. In Chapter 2 and Chapter 3, we address two challenges in spatio-temporal change-point detection: (i) how to deal with data with high dimensionality, and (ii) how to capture spatial and temporal correlations. Bayesian optimization is a prevalent approach for optimization problems defined by expensive-to-evaluate black-box functions. In Chapter 4, we develop a practical algorithm for optimization problems with black-box objective function and constraints.
    URI
    http://hdl.handle.net/1853/61247
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    • Georgia Tech Theses and Dissertations [23877]
    • School of Industrial and Systems Engineering Theses and Dissertations [1457]

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