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    • 7. 发明申请
    • EFFICIENT COMPUTATION FOR BAYESIAN OPTIMIZATION
    • WO2022242565A1
    • 2022-11-24
    • PCT/CN2022/092724
    • 2022-05-13
    • ALIBABA (CHINA) CO., LTD
    • HUANG, Yijun
    • G06F30/20
    • Systems and methods implement a modular computing environment for Bayesian optimization, decoupling steps of Bayesian optimization across multiple modules; minimizing inter-module dependency; extending functionality of each module; and reusing computing resources and intermediate results within each module. Variable hyperparameterization may reduce computational costs of optimization iterations, while also averting overfitting and destabilization of the Gaussian kernel based on sparser observations of the objective function. Computational complexity of updating the Gaussian kernel may be reduced from the cube to the square of the set of sampled outputs, by deferring computing updates to each hyperparameter while the optimization iterations are ongoing. Furthermore, repeated allocation and release of memory, repeated writing of data in memory to non-volatile storage, and repeated reading of data in non-volatile storage to memory across multiple optimization iterations may be averted, thereby alleviating multiple categories of computing resources, including processing power, memory, storage, from excess performance load.