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..
Heterogeneous Multi-output Gaussian Process Prediction
Authors: Pablo Moreno-Muñoz, Antonio Artés, Mauricio Álvarez
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the performance of the model on synthetic data and two real datasets: a human behavioral study and a demographic high-dimensional dataset. |
| Researcher Affiliation | Academia | 1Dept. of Signal Theory and Communications, Universidad Carlos III de Madrid, Spain 2Dept. of Computer Science, University of Sheffield, UK |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The code is publicly available in the repository github.com/pmorenoz/Het MOGP/ |
| Open Datasets | Yes | We preprocessed it to translate all property addresses to latitude-longitude points. For each spatial input, we considered two observations, one binary and one real. The first one indicates if the property is or is not a flat (zero would mean detached, semi-detached, terraced, etc.. ), and the second one the sale price of houses. Our goal is to predict features of houses given a certain location in the London area. We used a training set of N = 20, 000 samples, 1, 000 for test predictions and M = 100 inducing points. (https://www.gov.uk/government/collections/price-paid-data). We use our model for predicting a binary output (gender) and a continuous output (logarithmic age) and we compared against independent Chained GPs per output. The binary output is modelled as a Bernoulli distribution and the continuous one as a Gaussian. We obtained an average NLPD value of 0.0191 for both multi-output and independent output models with a slight difference in the standard deviation. (http://archive.ics.uci.edu/ml/) |
| Dataset Splits | Yes | We use a dataset of dimensionality p = 255 and 452 samples that we divide in training, validation and test sets (more details are in the appendix). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software components like "LBFGS-B algorithm", "ADADELTA included in the climin library", and "Python implementation" but does not provide specific version numbers for these components. |
| Experiment Setup | Yes | For all the experiments, we consider an RBF kernel for each covariance function kq( , ) and we set Q = 3. For standard optimization we used the LBFGS-B algorithm. When SVI was needed, we considered ADADELTA included in the climin library, and a mini-batch size of 500 samples in every output. |