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..
Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions
Authors: Kaito Fujii
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments in sparse regression and structure learning of graphical models to confirm the prac tical efficiency of the proposed local search algorithms. |
| Researcher Affiliation | Academia | 1National Institute of Informatics, Tokyo, Japan. Correspon dence to: Kaito Fujii <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Local search algorithms for a matroid con straint. ... Algorithm 2 Local search algorithms for a p-matroid inter section or p-exchange system constraint (p 2). |
| Open Source Code | No | The paper does not contain an explicit statement or link providing concrete access to the source code for the methodology described. |
| Open Datasets | No | The paper mentions generating synthetic datasets (e.g., "We generate synthetic datasets with a partition matroid constraint.") but does not provide concrete access information such as a link, DOI, or a citation to a publicly available dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages or sample counts for training, validation, and test sets) to reproduce the data partitioning. |
| Hardware Specification | Yes | We conduct the experiments in a machine with Intel Xeon E3 1225 V2 (3.20 GHz and 4 cores) and 16 GB RAM. |
| Software Dependencies | No | All the algorithms are implemented in Python 3.6. ... We use the L-BFGS-G solver in scipy.optimize li brary for evaluating the value of f. ... max-weight matching problem solver in Netwerk X library. The paper specifies Python 3.6, but does not provide version numbers for the other mentioned libraries (scipy.optimize, Netwerk X). |
| Experiment Setup | Yes | We set (n, d, nc, np) = (200, 50, 5, 5) in one setting and (n, d, nc, np) = (1000, 100, 10, 10) in the other setting. ... We apply these methods to a partition matroid constraint with capacity n0 p for each n0 2 {1, , 10}. ... We set (|V |, d) = (10, 5) in one setting and (|V |, d) = (20, 7) in the other setting. ... We apply these methods to pseudo-log-likelihood maximization under a b-matching constraint for each b 2 {1, , d}. |