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..
Challenges in Resource and Cost Allocation
Authors: Toby Walsh
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that such proxies, especially the more complex ones can work well in practice. |
| Researcher Affiliation | Academia | Toby Walsh NICTA and UNSW Sydney, Australia |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for its methodology. |
| Open Datasets | No | The paper mentions experiments but does not provide concrete access information or citations for any publicly available or open datasets used in its own experiments. |
| Dataset Splits | No | The paper does not provide specific dataset split information for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameter values or training configurations. |