Variational Inference for sparse network reconstruction from count data
Authors: Julien Chiquet, Stephane Robin, Mahendra Mariadassou
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We introduce the model in Section 2, the variational inference procedure in Section 3, simulation results in Section 4 and a reanalysis of two datasets in Section 5 |
| Researcher Affiliation | Academia | 1MIA 518, Agro Paritech/INRA, Universit e Paris-Saclay, Paris, France 2Ma IAGE, INRA, Universit e Paris-Saclay, Jouyen-Josas, France. |
| Pseudocode | No | The paper describes the 'Inference Algorithm' in Section 3.2 but presents it in paragraph form with mathematical expressions rather than structured pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | We implemented our algorithm in a R/C++ package PLNmodels, available on github https: //github.com/jchiquet/PLNmodels. |
| Open Datasets | Yes | Data were downloaded from the French open data platform data.gouv.fr and filtered to remove stations with no votes. |
| Dataset Splits | No | The paper describes using resampling and subsampling (e.g., 'B = 50 subsamples of size m = 10 n') for model selection (StARS) and mentions varying sample size 'n' in simulations. However, it does not specify explicit training, validation, and test dataset splits with percentages or sample counts for model reproduction. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions software like the 'PLNmodels R/C++ package', 'nlopt library', 'glassofast R package', and 'RNAseq Net R-package', but it does not specify version numbers for any of them. |
| Experiment Setup | Yes | We fix the number of variables to p = 50 in all our experiments. The sampling efforts Ni are drawn from a negative binomial distribution with mean 1000 and variance 1000 + 10002/ν. We always set v = 0.3, u = 0.1 in our simulations. We use B = 50 subsamples of size m = 10 n and 2β = 0.05 as default, as suggested in Liu et al. Here, we replace loglik in EBIC by its variational surrogate J(Y; ˆΩ) and use γ = 0.5 as recommended by Foygel & Drton for GGM. |