Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression
Authors: Tong Teng, Jie Chen, Yehong Zhang, Bryan Kian Hsiang Low5997-6004
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate the performance of our VBKS algorithm on synthetic and massive real-world datasets. |
| Researcher Affiliation | Academia | Tong Teng,1 Jie Chen,2 Yehong Zhang,1 Bryan Kian Hsiang Low1 1Department of Computer Science, National University of Singapore, Republic of Singapore 2College of Computer Science and Software Engineering, Shenzhen University, P. R. China {tengtong, yehong, lowkh}@comp.nus.edu.sg, chenjie@szu.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We empirically evaluate the performance of our VBKS algorithm on synthetic and two massive real-world datasets: (a) Swissgrid dataset4 contains 210, 336 records... (b) indoor environmental quality (IEQ) dataset5 contains temperature ( C) ... 4https://www.swissgrid.ch 5http://db.csail.mit.edu/labdata/labdata.html |
| Dataset Splits | No | The paper mentions test sets but does not specify train/validation/test dataset splits needed to reproduce the experiment. |
| Hardware Specification | Yes | The real-world experiments are performed on a Linux system with 5 Nvidia Ge Force GTX 1080 GPUs. |
| Software Dependencies | Yes | Stochastic optimization for VBKS is performed in a distributed manner over the 5 GPUs using GPflow (Matthews et al. 2017). |
| Experiment Setup | Yes | In all the synthetic experiments, we use |U| = 16 inducing inputs and a batch size | D| = 32 to perform the SGA update per iteration. ... For Swissgrid Dataset. We use |U| = 800 inducing inputs and a batch size | D| = 128 for SGA update per iteration. ... For IEQ Dataset. ... We use |U| = 1000 inducing inputs and a batch size | D| = 512 for SGA update per iteration. |