When Samples Are Strategically Selected
Authors: Hanrui Zhang, Yu Cheng, Vincent Conitzer
ICML 2019 | Conference PDF | Archive PDF | Plain Text | 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 <hrzhang@cs.duke.edu>, Yu Cheng <yucheng@cs.duke.edu>, Vincent Conitzer <conitzer@cs.duke.edu>. |
| 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. |