Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Authors: Zhenwen Dai, Mauricio Álvarez, Neil Lawrence
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that LVMOGP significantly outperforms related Gaussian process methods on various tasks with both synthetic and real data. ... We evaluate the performance of the proposed model with both synthetic and real data. ... We compare the performance of the proposed method with GP with independent observations and the linear model of coregionalization (LMC) on synthetic data, where the ground truth is known. |
| Researcher Affiliation | Collaboration | Zhenwen Dai zhenwend@amazon.com Mauricio A. Álvarez mauricio.alvarez@sheffield.ac.uk Neil D. Lawrence lawrennd@amazon.com Inferentia Limited. Dept. of Computer Science, University of Sheffield, Sheffield, UK. Amazon.com. |
| Pseudocode | No | The paper does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper states, 'The scientific idea and a preliminary version of code were developed prior to joining Amazon.' This is not an explicit statement of code release for the presented work, nor does it provide a link. |
| Open Datasets | Yes | We apply our method to a servo modeling problem, in which the task is to predict the rise time of a servomechanism in terms of two (continuous) gain settings and two (discrete) choices of mechanical linkages [Quinlan, 1992]. ... We take a in-house multi-sensor recordings including a list of sensor measurements such as temperature, carbon dioxide, humidity, etc. [Zamora-Martínez et al., 2014]. |
| Dataset Splits | Yes | The dataset contains 100 different uniformly sampled input locations (50 for training and 50 for testing), where each corresponds to 40 different conditions. ... We took 70% of the dataset as training data and the rest as test data, and randomly generated 20 partitions. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | We assumed a 2 dimensional latent space and set MH = 30 and MX = 10. ... We applied LVMOGP with a two-dimensional latent space with an ARD kernel and used five inducing points for the latent space and 10 inducing points for the function. ... We apply LVMOGP with missing data with the settings: QH = 2, MH = 10 and MX = 100. |