Scaling Bayesian Optimization for High-Dimensional Iterative Experimental Design

Author/​Artist
Semelhago, Andrew [Browse]
Format
Senior thesis
Language
English

Availability

Available Online

Copies in the Library

Location Call Number Status Location Service Notes
Mudd Manuscript Library - StacksAC102 Browse related items On-site accessReading Room Request

    Details

    Advisor(s)
    Engelhardt, Barbara [Browse]
    Department
    Princeton University. Department of Operations Research and Financial Engineering [Browse]
    Certificate
    Princeton University. Program in Finance [Browse]
    Class year
    2017
    Restrictions note
    Walk-in Access. This thesis can only be viewed on computer terminals at the Mudd Manuscript Library.
    Summary note
    Bayesian optimization (BO) is an intelligent search technique for optimizing expensive nonlinear black-box objective functions. It is tempting to apply BO to iterative experimental design in the physical sciences. But these scenarios often have high dimensionality, presenting two problems: first, the large amount of time the algorithm takes to generate suggestions for subsequent experiments, and, second, the prohibitively large number of expensive experiments needed to thoroughly search the parameter space. We present a new approach to mitigate both issues with Bayesian optimization for high-dimensional problems. The proposed solution involves changing the prior model of the black-box objective function and developing a local optimization approach for the acquisition function. We evaluate these solutions on high-dimensional optimization tasks and on a computational analogy to a biological experimental design task: CRISPR/Cas9 guide RNA sequence optimization. In categorical spaces, the random forests prior model leads to fast convergence, whereas in continuous spaces, the Gaussian process prior performs best. However, random forests generate suggestions much more quickly than Gaussian processes. Local optimization (LO) improves performance across the board in exchange for a small constant time increase. When gradient information is available, gradient descent methods with momentum accentuate this performance improvement.
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    Supplementary Information