Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes
Authors: Anton Mallasto, Aasa Feragen
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate our framework through experimental validation on GP datasets representing brain connectivity and climate development. |
| Researcher Affiliation | Academia | Anton Mallasto Department of Computer Science University of Copenhagen mallasto@di.ku.dk Aasa Feragen Department of Computer Science University of Copenhagen aasa@di.ku.dk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | A MATLAB library for relevant computations will be published at https://sites.google.com/view/antonmallasto/software. |
| Open Datasets | Yes | The white-matter tract GPs are estimated for a single subject from the Human Connectome Project [15, 32, 35], using probabilistic shortest-path tractography. From daily minimum temperatures measured at a set of 30 randomly sampled Russian metereological stations [9, 34] |
| Dataset Splits | No | The paper describes the datasets used but does not provide specific details on training, validation, or test data splits. |
| Hardware Specification | Yes | All code for computing Wasserstein distances and barycenters was implemented in MATLAB and ran on a laptop with 2,7 GHz Intel Core i5 processor and 8 GB 1867 MHz DDR3 memory. |
| Software Dependencies | No | The paper states that the code was implemented in MATLAB but does not specify a version number for MATLAB or any other software dependencies. |
| Experiment Setup | No | The paper describes general methods for GP estimation (e.g., 'using maximum likelihood parameters') and permutation testing, but it does not provide concrete hyperparameter values or detailed training configurations for the experimental setup. |