Multi-resolution Multi-task Gaussian Processes
Authors: Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang, Mark Girolami
NeurIPS 2019 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the competitiveness of MRGPs on synthetic settings and on the challenging problem of hyper-local estimation of air pollution levels across London from multiple sensing modalities operating at disparate spatio-temporal resolutions. |
| Researcher Affiliation | Academia | Oliver Hamelijnck The Alan Turing Institute Department of Computer Science University of Warwick EMAIL Theodoros Damoulas The Alan Turing Institute Depts. of Computer Science & Statistics University of Warwick EMAIL Kangrui Wang The Alan Turing Institute Department of Statistics University of Warwick EMAIL Mark A. Girolami The Alan Turing Institute Department of Engineering University of Cambridge EMAIL |
| Pseudocode | Yes | Algorithm 1 Inference of MR-GPRN [...] Algorithm 2 Inference procedure for MR-DGP |
| Open Source Code | Yes | Further analysis is provided in the Appendix and code is available at https: //github.com/ohamelijnck/multi_res_gps. [...] Codebase and datasets to reproduce results are available at https://github.com/ohamelijnck/multi_ res_gps |
| Open Datasets | Yes | observations coming from ground point sensors from the London Air Quality Network (LAQN). These sensors provide hourly readings of NO2. Secondly we use observations arising from a global satellite model [17] that provide hourly data at a spatial resolution of 7km 7km and provide 48 hour forecasts. [...] Codebase and datasets to reproduce results are available at https://github.com/ohamelijnck/multi_ res_gps |
| Dataset Splits | No | The paper mentions removing a 2-day region for testing and uses temporal ranges for training and prediction, but it does not specify explicit training/validation/test split percentages, sample counts, or a detailed splitting methodology for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper states 'Experiments are coded in Tensor Flow' but does not provide specific version numbers for TensorFlow or any other software dependencies. |
| Experiment Setup | No | The paper mentions optimizing variational and hyperparameters for training and a layer-by-layer optimization strategy for MR-DGP, but it does not provide specific numerical values for hyperparameters (e.g., learning rate, batch size, number of epochs) or detailed system-level training settings in the main text. |