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Poster
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
Fengxue Zhang · Jialin Song · James Bowden · Alexander Ladd · Yisong Yue · Thomas Desautels · Yuxin Chen

Thu Jul 27 04:30 PM -- 06:00 PM (PDT) @ Exhibit Hall 1 #111
in Poster Session 6 »

We study Bayesian optimization (BO) in high-dimensional and non-stationary scenarios. Existing algorithms for such scenarios typically require extensive hyperparameter tuning, which limits their practical effectiveness. We propose a framework, called BALLET, which adaptively filters for a high-confidence region of interest (ROI) as a superlevel-set of a nonparametric probabilistic model such as a Gaussian process (GP). Our approach is easy to tune, and is able to focus on local region of the optimization space that can be tackled by existing BO methods. The key idea is to use two probabilistic models: a coarse GP to identify the ROI, and a localized GP for optimization within the ROI. We show theoretically that BALLET can efficiently shrink the search space, and can exhibit a tighter regret bound than standard BO without ROI filtering. We demonstrate empirically the effectiveness of BALLET on both synthetic and real-world optimization tasks.

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Author Information

Fengxue Zhang (University of Chicago (SSO))
Jialin Song (NVIDIA)
James Bowden (Caltech)
Alexander Ladd (Lawrence Livermore National Labs)
Yisong Yue (Caltech & Latitude AI)
Yisong Yue

Yisong Yue is a Professor of Computing and Mathematical Sciences at Caltech and (via sabbatical) a Principal Scientist at Latitude AI. His research interests span both fundamental and applied pursuits, from novel learning-theoretic frameworks all the way to deep learning deployed in autonomous driving on public roads. His work has been recognized with multiple paper awards and nominations, including in robotics, computer vision, sports analytics, machine learning for health, and information retrieval. At Latitude AI, he is working on machine learning approaches to motion planning for autonomous driving.

Thomas Desautels (Lawrence Livermore National Labs)
Yuxin Chen (University of Chicago)

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