Summary of Bayesian Optimization Through Gaussian Cox Process Models For Spatio-temporal Data, by Yongsheng Mei et al.
Bayesian Optimization through Gaussian Cox Process Models for Spatio-temporal Data
by Yongsheng Mei, Mahdi Imani, Tian Lan
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Functional Analysis (math.FA); Probability (math.PR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Bayesian optimization (BO) has been a leading strategy for efficiently optimizing expensive-to-evaluate functions. However, existing methods rely on Gaussian process (GP) surrogate models and are not applicable to doubly-stochastic Gaussian Cox processes, where the observation process is modulated by a latent intensity function modeled as a GP. This paper proposes a novel maximum a posteriori inference of Gaussian Cox processes using Laplace approximation and change of kernel technique, enabling functional posterior estimation of the latent intensity function and covariance. The result is used to develop a BO framework based on the Gaussian Cox process model and a Nyström approximation for efficient computation. Evaluations on synthetic and real-world datasets demonstrate significant improvement over state-of-the-art inference solutions and effective BO with various acquisition functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about improving how we optimize things that are hard to measure. Right now, we use something called Bayesian optimization (BO), but it has limitations when dealing with certain types of data. The researchers in this paper came up with a new way to analyze this data, which allows us to get more accurate results and make better predictions. They also developed a new framework for optimizing things, which can be used in many different situations. This is important because it could lead to breakthroughs in areas like medicine or finance. |
Keywords
* Artificial intelligence * Inference * Optimization
