Elementary Estimators for Graphical Models
Authors: Eunho Yang, Aurelie C. Lozano, Pradeep K Ravikumar
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We corroborate this statistical performance, as well as significant computational advantages via simulations of both discrete and Gaussian graphical models. 6 Experiments In this section, we report a set of synthetic experiments corroborating our theoretical results on both Gaussian and discrete graphical models. |
| Researcher Affiliation | Collaboration | Eunho Yang IBM T.J. Watson Research Center eunhyang@us.ibm.com Aur elie C. Lozano IBM T.J. Watson Research Center aclozano@us.ibm.com Pradeep Ravikumar University of Texas at Austin pradeepr@cs.utexas.edu |
| Pseudocode | No | The paper describes the mathematical formulations and properties of the estimators but does not present any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide links to a code repository for the described methodology. |
| Open Datasets | No | To generate true inverse covariance matrices with a random sparsity structure, we follow the procedure described in [25, 24]. For each case, the size of the alphabet is set to m = 3; the true parameter vector is generated by sampling each non-zero entry from N(0, 1). |
| Dataset Splits | No | The paper mentions "cross-validation" for selecting a tuning parameter, but it does not specify a train/validation/test split for a fixed dataset, as the data is generated synthetically for each run. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using "QUIC algorithms [24]" but does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | We fix the thresholding parameter = 2.5 log p/n for all settings, and vary the regularization parameter λn = K log p/n to investigate how this regularizer affects the final estimators. the size of the alphabet is set to m = 3; the tuning parameter is set to λn = c log p/n, where c is selected using cross-validation. |