Order Statistics for Probabilistic Graphical Models
Authors: David Smith, Sara Rouhani, Vibhav Gogate
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally compared our new algorithm with a baseline sampling algorithm over randomly generated graphical models as well as Chow-Liu trees [Chow and Liu, 1968] computed over three benchmark datasets. We found that our new algorithm significantly outperforms the sampling algorithm, especially when r is not extreme (either too small or too large)." and "7 Experimental Results In this section, we aim to evaluate the performance of Algorithms 1 and 3. |
| Researcher Affiliation | Academia | David Smith, Sara Rouhani, and Vibhav Gogate The University of Texas at Dallas dbs014200@utdallas.edu, sxr15053@utdallas.edu, vgogate@hlt.utdallas.edu |
| Pseudocode | Yes | Algorithm 1 Find Median Independent Markov Network", "Algorithm 2 Estimate Rank", "Algorithm 3 Rank Variable Elimination", "Algorithm 4 Rank VE Step", "Algorithm 5 Rank VE Step", "Algorithm 6 Rank Variable Elimination Combine Bin Step |
| Open Source Code | No | No explicit statement providing concrete access (e.g., repository link, explicit release statement, or mention of supplementary materials) to the source code for the described methodology was found. |
| Open Datasets | Yes | We evaluate the infer rank query on 3 benchmark datasets commonly used for evaluating learning algorithms for tractable probabilistic models: NLTCS, KDD Cup, and Plants [Lowd and Davis, 2010; Rahman and Gogate, 2016; Gens and Domingos, 2013]. |
| Dataset Splits | No | No explicit details on training, validation, or test dataset splits (e.g., percentages, sample counts, or specific predefined splits) were found. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments were provided in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names with versions, solver versions) were provided in the paper. |
| Experiment Setup | Yes | We use quantization function q(x, α) = α log x, and run the experiment for varying settings of α." and "For each e [0, 80], we generate 100 Markov networks on 20 variables with e pairwise potentials having randomly generated scopes. The weights of each potential are randomly generated from N(0, 1). |