Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities
Authors: Arun Suggala, Mladen Kolar, Pradeep K. Ravikumar
NeurIPS 2017 | Venue PDF | 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 EMAIL Mladen Kolar University of Chicago Chicago, IL 60637 EMAIL Pradeep Ravikumar Carnegie Mellon University Pittsburgh, PA 15213 EMAIL |
| 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. |