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 Linear Extensions in Practice: MCMC Versus Exponential Monte Carlo
Authors: Topi Talvitie, Kustaa Kangas, Teppo Niinimäki, Mikko Koivisto
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This work presents an empirical evaluation of the state-of-the-art schemes and investigates a number of ideas to enhance their performance. ... Through the empirical evaluation, we show that approximate counting is feasible on posets up to a few hundred elements. |
| Researcher Affiliation | Academia | Topi Talvitie Dept. of Computer Science University of Helsinki topi.talvitie@helsinki.fi Kustaa Kangas Dept. of Computer Science Aalto University juho-kustaa.kangas@aalto.fi Teppo Niinim aki Dept. of Computer Science Aalto University teppo.niinimaki@aalto.fi Mikko Koivisto Dept. of Computer Science University of Helsinki mikko.koivisto@helsinki.fi |
| Pseudocode | No | The paper describes algorithms verbally and with illustrative diagrams (e.g., Figure 2) but does not include formal pseudocode blocks or algorithms. |
| Open Source Code | Yes | We will make these instances as well as all algorithm implementations publicly available.1 (footnote refers to github.com/ttalvitie/le-counting-practice) |
| Open Datasets | Yes | Posets extracted from benchmark Bayesian networks, obtained from the Bayesian Network Repository (www.cs. huji.ac.il/ galel/Repository). We generated five posets from each instance class and size between 8 and 512 elements. We will make these instances as well as all algorithm implementations publicly available.1 (footnote refers to github.com/ttalvitie/le-counting-practice) |
| Dataset Splits | No | The paper does not mention specific training/validation/test dataset splits. It evaluates algorithms on generated instances and benchmark posets without describing such partitioning for model training. |
| Hardware Specification | No | Each run was limited to 24 hours of CPU time and 8 GB of RAM; all running times were measured. No specific CPU models, GPU models, or detailed hardware specifications are provided. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks, or solvers with version numbers) are mentioned in the paper. |
| Experiment Setup | Yes | Each algorithm was instantiated to produce a (1, 1/4)-approximation for the number of linear extensions, i.e., an estimate within a factor of 2 with probability at least 3/4. In the experiments we set α = 0.1, β = 10, and κ = 5, observed to perform well on average. |