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..
Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search
Authors: Shinsaku Sakaue, Taihei Oki
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a theoretical paper and no negative societal impacts are expected. |
| Researcher Affiliation | Academia | Shinsaku Sakaue The University of Tokyo Tokyo, Japan EMAIL Taihei Oki The University of Tokyo Tokyo, Japan EMAIL |
| Pseudocode | Yes | Algorithm 1 GBFS with heuristic function values ρ Algorithm 2 A* with heuristic function values ρ |
| Open Source Code | No | The reproducibility checklist states: "(a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]" |
| Open Datasets | No | The paper is theoretical and does not use or provide access information for a specific public dataset. It mentions: "assuming that path-finding instances defined on a fixed vertex set of size n are drawn i.i.d. from an unknown distribution." |
| Dataset Splits | No | The paper is theoretical and does not describe experimental data splits (training, validation, test). The reproducibility checklist states: "(b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]" |
| Hardware Specification | No | The paper is theoretical and does not involve empirical experiments requiring hardware specifications. The reproducibility checklist states: "(d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]" |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers for empirical experiments. The reproducibility checklist states: "(b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]" |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. The reproducibility checklist states: "(b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]" |