Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs
Authors: Yusuke Tanaka, Toshiyuki Tanaka, Tomoharu Iwata, Takeshi Kurashima, Maya Okawa, Yasunori Akagi, Hiroyuki Toda
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on real-world data sets demonstrate that our model can 1) accurately refine coarse-grained areal data, and 2) offer performance improvements by using the areal data sets from multiple domains. |
| Researcher Affiliation | Collaboration | Yusuke Tanaka1,3, Toshiyuki Tanaka3, Tomoharu Iwata2, Takeshi Kurashima1, Maya Okawa1, Yasunori Akagi1, Hiroyuki Toda1 1NTT Service Evolution Labs., 2NTT Communication Science Labs., 3Kyoto University |
| 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 its own source code or a direct link to a repository containing the implementation of the described methodology. |
| Open Datasets | Yes | We evaluated SAGP using 10 and 3 real-world areal data sets from two cities, New York City and Chicago, respectively. They were obtained from NYC Open Data 2 and Chicago Data Portal 3. 2https://opendata.cityofnewyork.us 3https://data.citychicago.org/ |
| Dataset Splits | Yes | The number L of the latent GPs was chosen from {1, . . . , S} via leave-one-out cross-validation [1]; the validation error was obtained using each held-out coarse-grained data value. |
| Hardware Specification | Yes | The average computation times for inference were 1728.2 and 115.1 seconds for the data sets from New York City and Chicago, respectively; the experiments were conducted on a 3.1 GHz Intel Core i7. |
| Software Dependencies | No | The paper mentions the use of 'Sci Py' for the L-BFGS method but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | In our experiments, we used zero-mean Gaussian processes as the latent GPs {gl(x)}L l=1, i.e., l(x) = 0 for l = 1, . . . , L. We used the following squared-exponential kernel as the covariance function for the latent GPs, γl(x, x0) = 2 l exp( kx x0k2/2β2 l )... Here, we set 2 l = 1 because the variance can already be represented by scaling the columns of W. For simplicity, the covariance function for the Gaussian noise process n(x, x0) is set to (x, x0) = diag(λ2 1δ(x x0), . . . , λ2 Sδ(x x0)), where δ( ) is Dirac s delta function. The model parameters, W, {λs}S s=1, , {βl}L l=1, were learned by maximizing the logarithm of the marginal likelihood (8) or (12) using the L-BFGS method [15] implemented in Sci Py (https://www.scipy.org/). For approximating the integral over regions (see (18)), we divided a total region of each city into sufficiently fine-grained square grid cells, the size of which was 300 m 300 m for both cities... The number L of the latent GPs was chosen from {1, . . . , S} via leave-one-out cross-validation... |