Variational Inference for Gaussian Process Modulated Poisson Processes
Authors: Chris Lloyd, Tom Gunter, Michael Osborne, Stephen Roberts
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The resulting algorithm is shown to outperform standard methods on synthetic examples, coal mining disaster data and in the prediction of Malaria incidences in Kenya. |
| Researcher Affiliation | Academia | Chris Lloyd* CLLOYD@ROBOTS.OX.AC.UK Tom Gunter* TGUNTER@ROBOTS.OX.AC.UK Michael A. Osborne MOSB@ROBOTS.OX.AC.UK Stephen J. Roberts SJROB@ROBOTS.OX.AC.UK Department of Engineering Science, University of Oxford |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. The methods are described using mathematical formulas and prose. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It only mentions "GPy (The GPy authors, 2014)" which is a third-party framework. |
| Open Datasets | Yes | These data, first analysed in this form in 1979 (Jarrett, 1979) Malaria Atlas Project (2014) http://www.map.ox.ac.uk/ explore/data-modelling/, 2014. [Online; accessed 24-October-2014] |
| Dataset Splits | No | The paper does not explicitly provide validation dataset split information. It mentions creating "training and test subsets by allocating each point to either subset with probability 0.5" but no dedicated validation set split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions "GPy (The GPy authors, 2014)" as a toolkit for future integration, but it does not specify software dependencies with version numbers used for the reported experiments. |
| Experiment Setup | No | The paper describes the model setup and optimization process but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rates, batch sizes, number of epochs) or specific training configurations. |