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..
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees
Authors: AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang3664-3671
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We generated 100 random UCCGs of order p = 20, , 60 with r p 2 as the number of edges based on the procedure proposed in (He, Jia, and Yu 2015), where parameter r controls the graph density. We compared the proposed algorithm with the counting algorithm in (He, Jia, and Yu 2015) in Table 1. Figure 3 depicts SDNE versus the number of samples. |
| Researcher Affiliation | Academia | 1Department of ECE, University of Illinois at Urbana-Champaign, Urbana, IL, USA 2Electrical Engineering Department, Sharif University of Technology, Tehran, Iran 3Departments of ECE and ISE, University of Illinois at Urbana-Champaign, Urbana, IL, USA 4Department of Philosophy, Carnegie Mellon University, Pittsburgh, USA |
| Pseudocode | Yes | Algorithm 1 MEC Size Calculator, Algorithm 2 Uniform Sampler |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | We generated 100 random UCCGs of order p = 10, 20, 30, with r p 2 edges, where r = 0.2. The paper does not state that this generated data is publicly available. |
| Dataset Splits | No | The paper does not explicitly provide training, validation, or test dataset splits for its own experimental setup. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers used for its experiments. |
| Experiment Setup | No | The paper describes the generation of random graphs for simulation, but it does not specify any experimental setup details such as hyperparameters, optimization settings, or model initialization for a learning task. |