Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior
Authors: Anh Tong, Toan M Tran, Hung Bui, Jaesik Choi9906-9914
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experimental Evaluations In this section, we first set up choices for compositional kernels. We then justify how the Horseshoe assumption for kernel selection on synthetic data as well as time series data. Finally, we validate our model with regression and classification tasks. |
| Researcher Affiliation | Collaboration | Anh Tong1, Toan M Tran2, Hung Bui2, Jaesik Choi3,4 1 Ulsan National Institute of Science and Technology 2 Vin AI Research 3 Korea Advanced Institute of Science and Technology 4 INEEJI |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a direct link to a code repository for the described methodology. The footnote refers to an extended arXiv version, which also does not contain a code link. |
| Open Datasets | Yes | We conducted experiments on UCI data sets (Asuncion and Newman 2007) including boston, concrete, energy, kin8nm, wine and yatch (see Appendix for detailed descriptions). ... We test our model on GEFCOM data set from the Global Energy Forecasting Competition (Tao Hong, Pierre Pinson, and Shu Fan 2014). |
| Dataset Splits | No | The paper mentions training and testing splits (e.g., '90% of the data set for training and held out 10% as test data', 'test data is taken from top 1/15 and bottom 1/15 of the data, the remaining is train data') but does not specify a separate validation set or its split percentage. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | Our model is developed based on (Matthews et al. 2017). This implies use of GPflow/TensorFlow, but no specific version numbers for these or other software dependencies are provided. |
| Experiment Setup | No | The paper mentions hyperparameter initialization strategies but does not provide specific numerical values for hyperparameters (e.g., learning rate, batch size, number of epochs) or other detailed training configurations. |