High-dimensional Additive Gaussian Processes under Monotonicity Constraints
Authors: Andrés López-Lopera, Francois Bachoc, Olivier Roustant
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance and scalability of the methodology in several synthetic examples with hundreds of dimensions under monotonicity constraints as well as on a real-world flood application. Section 5 provides the numerical experiments. |
| Researcher Affiliation | Academia | Andrés F. López-Lopera CERAMATHS, UPHF 59313 Valenciennes, France andres.lopezlopera@uphf.fr François Bachoc IMT, UMR5219 CNRS Université Paul Sabatier 31062 Toulouse, France Olivier Roustant IMT, UMR5219 CNRS INSA Toulouse 31077 Toulouse, France |
| Pseudocode | Yes | Algorithm 1 summarizes the routine of Max Mod. Algorithm 1 Max Mod for additive c GPs |
| Open Source Code | Yes | Finally, we provide open-source codes for our full framework. Both R codes and notebooks to reproduce some of the numerical results are available in the Github repository: https://github.com/anfelopera/lineq GPR. |
| Open Datasets | No | The database contains a flood study conducted by the French multinational electric utility company EDF in the Vienne river [34]. The flood database is private. |
| Dataset Splits | No | As training data, we use random Latin hypercube designs (LHDs). The remaining data are used for testing the c GPs. (No explicit validation set or percentages/counts for splits). |
| Hardware Specification | Yes | Experiments throughout this section are executed on an 11th Gen Intel(R) Core(TM) i5-1145G7 2.60GHz 1.50 GHz, 16 Gb RAM. |
| Software Dependencies | Yes | Implementations of the additive c GP framework are based on the R package lineq GPR [31]. [31] A. F. López-Lopera, lineq GPR: Gaussian process regression models with linear inequality constraints, 2021, R package version 0.3.0. |
| Experiment Setup | Yes | Input parameters: > 0, 0 > 0, d. We set 5 knots per dimension. We fix = (σ2i , i)1 i d = (1, 2). The c GP mean is obtained by averaging 103 HMC samples. |