Distinguishing Distributions When Samples Are Strategically Transformed
Authors: Hanrui Zhang, Yu Cheng, Vincent Conitzer
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we give necessary and sufficient 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 hrzhang@cs.duke.edu Yu Cheng Duke University Durham, NC 27708 yucheng@cs.duke.edu Vincent Conitzer Duke University Durham, NC 27708 conitzer@cs.duke.edu |
| 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. |