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 [1].
Distinguishing Distributions When Samples Are Strategically Transformed
Authors: Hanrui Zhang, Yu Cheng, Vincent Conitzer
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we give necessary and suf๏ฌcient conditions for when the principal can distinguish between agents of good and bad types, when the type affects the distribution of samples that the agent has access to. We also study the computational complexity of checking these conditions. Finally, we study how many samples are needed. |
| Researcher Affiliation | Academia | Hanrui Zhang Duke University Durham, NC 27708 EMAIL Yu Cheng Duke University Durham, NC 27708 EMAIL Vincent Conitzer Duke University Durham, NC 27708 EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code related to the methodology described. |
| Open Datasets | No | The paper is theoretical and uses an illustrative example, not an actual dataset for training or evaluation. Therefore, no information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with data, so there is no mention of dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would involve specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments that would involve specific software dependencies. Therefore, no software dependency details are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |