Near-Optimal Active Learning of Multi-Output Gaussian Processes
Authors: Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan Kankanhalli
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation on real-world datasets shows that our proposed approach outperforms existing algorithms for active learning of MOGP and single-output GP models. |
| Researcher Affiliation | Academia | Department of Computer Science , Interactive Digital Media Institute National University of Singapore, Republic of Singapore {yehong, lowkh, mohan}@comp.nus.edu.sg , idmhtn@nus.edu.sg |
| Pseudocode | No | The paper describes the approximation algorithm using mathematical equations (9) but does not present it in a pseudocode block or algorithm box format. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Jura dataset (Goovaerts 1997) contains concentrations of 7 heavy metals collected at 359 locations in a Swiss Jura region; (b) Gilgai dataset (Webster 1977) contains electrical conductivity and chloride content generated from a line transect survey of 365 locations of Gilgai territory in New South Wales, Australia; and (c) indoor environmental quality (IEQ) dataset (Bodik et al. 2004) contains temperature ( F) and light (Lux) readings taken by 43 temperature sensors and 41 light sensors deployed in the Intel Berkeley Research lab. |
| Dataset Splits | No | The paper states that observations are 'randomly selected to form the test set T' but does not provide specific details on training, validation, or test splits, nor does it mention a validation set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions software tools like 'k-means' and 'MOGP and single-output GP models' but does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For all experiments, k-means is used to select inducing locations U by clustering all possible locations available to be selected for observation into |U| clusters such that each cluster center corresponds to an element of U. The hyper-parameters (i.e., σ2 si, σ2 ni, P0 and Pi for i = 1, . . . , M) of MOGP and single-output GP models are learned using the data via maximum likelihood estimation ( Alvarez and Lawrence 2011). For example, for Jura dataset, |U| = 50, 100, 200 are used. |