Statistics and Data Science Seminar
Huiling Liao
Illinois Institute of Technology
Rare Event Detection by Acquisition-Guided Sampling
Abstract: Motivated by the challenges in detecting extremely rare failures for sophisticated specifications in circuit design, we consider the problem of detecting regions of interest (ROIs) that consist of specifications with the value of a complex target function for the system performance being below or above a certain pre-specified threshold. Though Bayesian optimization (BO) has been applied to this problem, it is not effective in identifying multiple ROIs as it was originally designed for global optimization and tends to focus on searching the area where the global optimum is most likely to be. In this work, we propose a sampling strategy for fast ROI detection within a limited number of target function evaluations. The sampling distribution is designed so that the probability of a specification being sampled is proportional to the corresponding value of the acquisition function. Such an acquisition-guided sampling algorithm promotes a wider search of the sample space and a simpler incorporation of different criteria to determine the specifications to be evaluated next. To further improve the performance, we propose a new design of the acquisition function and two modifications of existing acquisition functions. Numerical studies on synthetic functions and a real-world circuit design application demonstrate that the proposed method can enjoy a stronger exploration ability provided by sampling and achieve faster ROI detection with higher coverage.
Wednesday February 26, 2025 at 4:00 PM in 636 SEO