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..
Unlabelled Sample Compression Schemes for Intersection-Closed Classes and Extremal Classes
Authors: Joachim Rubinstein, Benjamin Rubinstein
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | No experiments were run for this work. |
| Researcher Affiliation | Academia | J. Hyam Rubinstein School of Mathematics & Statistics University of Melbourne Parkville, VIC 3052, Australia EMAIL Benjamin I. P. Rubinstein School of Computing & Information Systems University of Melbourne Parkville, VIC 3052, Australia EMAIL |
| Pseudocode | Yes | Algorithm 1 Shortest-Path Closure |
| Open Source Code | No | No experiments were run for this work. We are not using existing assets or curating/releasing new assets. |
| Open Datasets | No | No experiments were run for this work. Thus, no dataset was used for training. |
| Dataset Splits | No | No experiments were run for this work. Thus, no training/test/validation dataset splits are described. |
| Hardware Specification | No | No experiments were run for this work. Thus, no hardware specification is provided. |
| Software Dependencies | No | No experiments were run for this work. Thus, no software dependencies are specified. |
| Experiment Setup | No | No experiments were run for this work. Thus, no experimental setup details are provided. |