Distributed Gaussian Processes
Authors: Marc Deisenroth, Jun Wei Ng
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically assess three aspects of all distribute GP models: (1) The required training time, (2) the approximation quality, (3) a comparison with state-of-the-art sparse GP methods. |
| Researcher Affiliation | Academia | Marc Peter Deisenroth M.DEISENROTH@IMPERIAL.AC.UK Department of Computing, Imperial College London, United Kingdom Jun Wei Ng JUNWEI.NG10@ALUMPERIAL.AC.UK Department of Computing, Imperial College London, United Kingdom |
| Pseudocode | No | The paper describes methods and computational structures but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | The Kin40K data set consists of 10,000 training points and 30,000 test points. We use the same split into training and test data as Seeger et al. (2003), L azaro Gredilla et al. (2010), and Nguyen & Bonilla (2014). For the US Flight Data, a link to the dataset is provided: http://stat-computing.org/dataexpo/2009/ |
| Dataset Splits | Yes | The Kin40K data set consists of 10,000 training points and 30,000 test points. We use the same split into training and test data as Seeger et al. (2003), L azaro Gredilla et al. (2010), and Nguyen & Bonilla (2014). For the US Flight Data, 'We selected the first P data points to train the model and the following 100,000 to test it.' |
| Hardware Specification | Yes | For the Kin40K experiment: 'a Virtual Machine with 16 3 GHz cores and 8 GB RAM.' For the US Flight Data: 'a workstation with 12 3.5 GHz cores and 32 GB RAM...' and 'All experiments can be conducted on a Mac Book Air (2012) with 8 GB RAM.' |
| Software Dependencies | No | The paper does not specify version numbers for any software components or libraries used. |
| Experiment Setup | No | The paper mentions using LBFGS for training and optimizing hyper-parameters but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations. |