Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
High-dimensional Additive Gaussian Processes under Monotonicity Constraints
Authors: Andrés López-Lopera, Francois Bachoc, Olivier Roustant
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 19,261 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,527 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL 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. |