The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities
Authors: Arun Suggala, Mladen Kolar, Pradeep K. Ravikumar
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experimental results on both synthetic and real datasets. We compare our estimator, Expxorcist, with the Nonparanormal model of [17] and Gaussian Graphical Model (GGM). |
| Researcher Affiliation | Academia | Arun Sai Suggala Carnegie Mellon University Pittsburgh, PA 15213 asuggala@cs.cmu.edu Mladen Kolar University of Chicago Chicago, IL 60637 mkolar@chicagobooth.edu Pradeep Ravikumar Carnegie Mellon University Pittsburgh, PA 15213 pradeepr@cs.cmu.edu |
| Pseudocode | No | The paper describes its algorithm in prose within Section 4, but does not present it as a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statements about the release of its source code or links to a code repository. |
| Open Datasets | Yes | This dataset was downloaded from http://www.kibot.com/. |
| Dataset Splits | Yes | We use the data collected in February 2010 as training data and data from March 2010 as held out data for tuning parameter selection. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'glasso' and 'two step estimator' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | For this experiment we set p = 50 and n {100, 200, 500} and varied the regularization parameter λ from 10 2 to 1. To fit the data to the non parametric model (3), we used cosine basis and truncated the basis expansion to top 30 terms. |