Bayesian Optimization through Gaussian Cox Process Models for Spatio-temporal Data
Authors: Yongsheng Mei, Mahdi Imani, Tian Lan
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations on various synthetic and real-world datasets demonstrate significant improvement over state-of-the-art inference solutions for Gaussian Cox processes, as well as effective BO with a wide range of acquisition functions designed through the underlying Gaussian Cox process model. |
| Researcher Affiliation | Academia | Yongsheng Mei1, Mahdi Imani2, Tian Lan1 1The George Washington University 2Northeastern Univeristy {ysmei, tlan}@gwu.edu, m.imani@northeastern.edu |
| Pseudocode | Yes | Algorithm 1 BO on the Gaussian Cox Process |
| Open Source Code | Yes | The code has been made available on Git Hub via https://github.com/ysmei97/gaussian cox bo. |
| Open Datasets | Yes | Extensive evaluations are conducted using both synthetic functions in the literature (Adams et al., 2009) and real-world spatio-temporal datasets, including DC crime incidents (DC.gov, 2022), 2D neuronal data (Sargolini et al., 2006), and taxi data of Porto (O Connell et al., 2015). |
| Dataset Splits | No | The paper discusses observed regions and initial observations, and iterates on adding new samples, but does not specify fixed training, validation, and test dataset splits with percentages or counts. |
| Hardware Specification | Yes | We conducted our experiments on the Ubuntu 20.04 system, with Intel(R) Core(TM) i7-6700 4-core CPU (3.4 GHz) and 16.0 GB RAM. |
| Software Dependencies | Yes | The algorithm is implemented in Python 3.8, using main Python libraries Num Py 1.22.3 and Pandas 2.0.3. |
| Experiment Setup | Yes | In the experiment, we initialize the region centers t = (25, 60) in the time domain of [0, 100] with a region radius of 2, i.e., region size of 4. Observations in selected regions are highlighted in red vertical bars. We use the UCB acquisition function for identifying intensity peaks. To expedite the BO process, we set the acquisition values of explored regions as zero to prevent the algorithm from becoming trapped within the same region. |