Variational Gaussian processes for linear inverse problems
Authors: Thibault RANDRIANARISOA, Botond Szabo
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 thibault.randrianarisoa@unibocconi.it; Botond Szabo Department of Decision Sciences Bocconi University via Roentgen 1, 20136, Milano, MI, Italy botond.szabo@unibocconi.it |
| 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. |