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..
When Samples Are Strategically Selected
Authors: Hanrui Zhang, Yu Cheng, Vincent Conitzer
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we introduce a theoretical framework for this problem and provide key structural and computational results. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Duke University, Durham, North Carolina, USA. Correspondence to: Hanrui Zhang <EMAIL>, Yu Cheng <EMAIL>, Vincent Conitzer <EMAIL>. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. It focuses on theoretical proofs and mathematical derivations. |
| Open Source Code | No | The paper does not provide any statements about open-source code availability or links to code repositories for the described methodology. |
| Open Datasets | No | The paper uses illustrative examples with abstract distributions (e.g., 'Let g(1) = 0.05 and b(1) = 0.005') rather than concrete, publicly available datasets. No access information (links, DOIs, citations to specific public datasets) is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments requiring dataset splits. No information regarding training, validation, or test splits is provided. |
| Hardware Specification | No | The paper does not describe any empirical experiments, and thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software (libraries, solvers) or their version numbers that would be required to replicate computational results. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not describe any empirical experiments, hence no experimental setup details, hyperparameters, or training configurations are provided. |