Learning the Structure of Large Networked Systems Obeying Conservation Laws
Authors: Anirudh Rayas, Rajasekhar Anguluri, Gautam Dasarathy
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we complement our theoretical results with experimental validation of the performance of the proposed estimator on synthetic and real-world data. |
| Researcher Affiliation | Academia | Anirudh Rayas Arizona State University ahrayas@asu.edu Rajasekhar Anguluri Arizona State University rangulur@asu.edu Gautam Dasarathy Arizona State University gautamd@asu.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The code and it s related details will be made available in the appendix. |
| Open Datasets | Yes | We validate the support recovery performance of our 1-regularized MLE on synthetic and a benchmark power distribution network (see Fig. 2). ... (ii) Power network: We set B to be the Laplacian of the IEEE 33 bus power distribution network [69]. |
| Dataset Splits | No | The paper mentions using samples of Y and averaging results over 100 trials but does not specify a train/validation/test split or cross-validation setup for specific datasets. |
| Hardware Specification | No | The paper states 'These [total amount of compute and type of resources] will be mentioned in the appendix', indicating that specific hardware details are not present in the current document. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We choose λn proportional to log p/n. Our results are averaged over 100 trials of n independent samples of Y. We compare 1-regularized MLE performance with (i) the square-root estimator (hereafter, GLASSO+SR) that identifies the support of B by determining (i, j) for which b 1 2 i,j 6= 0; and (ii) the GLASSO+2HR (Hop Refinement) estimator [20] that identifies the support of B by determining (i, j) for which b i,j for = 1e 02. |