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 [1].
A Faster Practical Approximation Scheme for the Permanent
Authors: Juha Harviainen, Mikko Koivisto
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical Results We empirically compared the proposed upper bound and preprocessing method against previous state of the art. |
| Researcher Affiliation | Academia | Juha Harviainen, Mikko Koivisto University of Helsinki EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Nesting rejection sampling |
| Open Source Code | Yes | Our implementations as well as the details of the computing infrastructure are included in the supplement. https://github.com/Kalakuh/nesting |
| Open Datasets | Yes | In addition, we tested the schemes on real-world matrices from Network Data Repository (Rossi and Ahmed 2015) licensed under CC-BY-SA. |
| Dataset Splits | No | The paper does not describe traditional train/validation/test dataset splits, as its focus is on approximation algorithms for matrix permanents, evaluated based on running time for a certain approximation quality, rather than training machine learning models on partitioned data. |
| Hardware Specification | No | The paper mentions that 'details of the computing infrastructure are included in the supplement' and provides a GitHub link, but it does not explicitly specify hardware details (e.g., GPU/CPU models, memory) within the main text. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | We followed the test setup of Harviainen et al. (2021) and evaluated our implementations by estimating the running time required for getting a (0.1, 0.05)-approximation, corresponding to 388 accepted draws with the GBAS algorithm. The estimate of the time was computed based on the time required for 65 accepted draws to save computation time. The running time was limited to 4825 seconds per instance |