Bounded-Loss Private Prediction Markets
Authors: Rafael Frongillo, Bo Waggoner
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we give such a mechanism: the first private prediction market framework with a bounded worst-case loss. Our construction and proof proceeds in two steps. |
| Researcher Affiliation | Collaboration | Rafael Frongillo Colorado Boulder raf@colorado.edu Bo Waggoner Microsoft Research bwag@colorado.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not mention using any specific datasets for training or evaluation. Therefore, it does not provide concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on datasets. Consequently, it does not specify any training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setups requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup such as hyperparameters or system-level training settings. |