Learning Utilities and Equilibria in Non-Truthful Auctions
Authors: Hu Fu, Tao Lin
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work is theoretical in nature, and should be understood as providing understanding for existing or possible practice, rather than having immediate societal impacts. |
| Researcher Affiliation | Academia | Hu Fu Department of Computer Science University of British Columbia Vancouver, BC V6T 1Z4 hufu@cs.ubc.ca Tao Lin Center on Frontiers of Computing Studies Department of Computer Science Peking University Beijing, China lin_tao@pku.edu.cn Tao Lin is now at Harvard University. This work was done when he was at Peking University and during a visit to the University of British Columbia. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and focuses on sample complexity for learning from distributions, not on conducting experiments on specific, publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation or dataset splits (train/validation/test). |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details like hyperparameters or training configurations. |