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].
Learning Utilities and Equilibria in Non-Truthful Auctions
Authors: Hu Fu, Tao Lin
NeurIPS 2020 | Venue PDF | 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 EMAIL Tao Lin Center on Frontiers of Computing Studies Department of Computer Science Peking University Beijing, China EMAIL 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. |