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..
Variational Gaussian processes for linear inverse problems
Authors: Thibault RANDRIANARISOA, Botond Szabo
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the applicability of the procedure in the numerical analysis of Section 4 and conclude the paper with discussion in Section 5. |
| Researcher Affiliation | Academia | Thibault Randrianarisoa Department of Decision Sciences Bocconi University via Roentgen 1, 20136, Milano, MI, Italy EMAIL; Botond Szabo Department of Decision Sciences Bocconi University via Roentgen 1, 20136, Milano, MI, Italy EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper uses synthetic data generated for the experiments: 'We set the sample size n = 8000, take uniformly distributed covariates on [0, 1), and let j cjj (1+β) sin(jπt), cj = 1 + 0.4 sin( 5πj), j odd, 2.5 + 2 sin( 2πj), j even, for β = 1. The independentobservations are generated as Yi N(Af0(xi), 1), depending on the solution of the forward map Af0 after time T = 10 2.' This is not a publicly available dataset. |
| Dataset Splits | No | The paper generates synthetic data for its numerical analysis and does not specify explicit training, validation, or test splits. It directly uses the generated data for evaluation. |
| Hardware Specification | Yes | The computations were carried out with a 2,6 GHz Quad-Core Intel Core i7 processor. |
| Software Dependencies | No | The paper does not specify any software names with version numbers used for the experiments (e.g., specific programming languages, libraries, or frameworks with their versions). |
| Experiment Setup | Yes | We set the sample size n = 8000, take uniformly distributed covariates on [0, 1), and let j cjj (1+β) sin(jπt), cj = 1 + 0.4 sin( 5πj), j odd, 2.5 + 2 sin( 2πj), j even, for β = 1. The independentobservations are generated as Yi N(Af0(xi), 1), depending on the solution of the forward map Af0 after time T = 10 2. We consider the prior with λj = e ξj2 for ξ = 10 1. We consider the population spectral feature method described in (10) and plot the variational approximation of the posterior for m = 6 and m = 3 inducing variables in Figure 1. |