Learning the piece-wise constant graph structure of a varying Ising model
Authors: Batiste Le Bars, Pierre Humbert, Argyris Kalogeratos, Nicolas Vayatis
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experimental results on several synthetic datasets and a real-world dataset demonstrate the performance of our method. |
| Researcher Affiliation | Academia | 1Universit e Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-91190 Gif-sur-Yvette, France. |
| Pseudocode | No | The paper describes the optimization program and methodology using mathematical formulations and descriptive text, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of TVI-FL is available online1 and provided in the supplementary material, so as a Jupyter Notebook reproducing results and figures of the real-world example. |
| Open Datasets | Yes | We analyze the different votes of the Illinois House of Representatives during the period of the 114-th and 115-th US Congresses (2015-2019), which are available at voteview.com (Lewis et al., 2020). To generate a piece-wise constant Ising model: We first pick a degree value d {2, 3, 4} and draw independently 3 random d-regular graphs, one for each submodel... For each submodel, we draw observations using Gibbs sampling with a burn-in period of 1000 samples. Moreover, we collect one observation every 20 samples (lag) to avoid dependencies between them. |
| Dataset Splits | Yes | The second technique, based on cross-validation (CV), assumes that more than one sample is observed at each moment in time i = 1, ..., n. Besides, to be able to perform CV, we also sample 5 more observations per timestamp and use them only in the testing phase. |
| Hardware Specification | No | All the experiments were implemented using Python and conducted on a personal laptop. This statement is too general and does not provide specific details on the CPU, GPU, or memory of the laptop. |
| Software Dependencies | No | All the experiments were implemented using Python... In this work, we use the python package CVXPY (Diamond & Boyd, 2016)... The paper mentions Python and CVXPY but does not provide specific version numbers for either. |
| Experiment Setup | Yes | We first pick a degree value d {2, 3, 4} and draw independently 3 random d-regular graphs... For each submodel, we draw observations using Gibbs sampling with a burn-in period of 1000 samples. Moreover, we collect one observation every 20 samples (lag) to avoid dependencies between them. For each experiment, we use a random-search strategy to find the best pair of hyperparameters (λ1, λ2) in [4, 15] [30, 40]. The first is the Akaike Information Criterion (AIC) that computes the average of the following quantity for all nodes: AIC(bβa) 2La(bβa) + 2 Dim(bβa). |