Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods
Authors: Seth Flaxman, Andrew Wilson, Daniel Neill, Hannes Nickisch, Alex Smola
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our methods on synthetic and real data, focusing on runtime and accuracy for inference and hyperparameter learning. |
| Researcher Affiliation | Collaboration | 1Carnegie Mellon University, 2Philips Research Hamburg, 3Marianas Labs |
| Pseudocode | Yes | Pseudocode for our algorithm is shown in Algorithm 1. |
| Open Source Code | Yes | We have implemented code as part of the GPML toolbox (Rasmussen & Nickisch, 2010). See http://www.cs.cmu.edu/ andrewgw/pattern for updates and demos. |
| Open Datasets | Yes | The City of Chicago makes geocoded, date-stamped crime report data publicly available through its data portal3. 3http://data.cityofchicago.org |
| Dataset Splits | Yes | We used 5-fold crossvalidation, relearning the hyperparameters for each fold and making predictions for the latent function values fi on the 20% of data that was held out. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper mentions implementing code as part of the 'GPML toolbox (Rasmussen & Nickisch, 2010)' but does not provide specific version numbers for this toolbox or any other software dependencies. |
| Experiment Setup | Yes | We ran non-linear conjugate gradient descent for 200 iterations. For hyperparameter learning, our spatial grid was 17 26, corresponding to 1 mile by 1 mile grid cells, and our temporal grid was one cell per week, for a total of 416 weeks. |