Dynamic Revenue Sharing
Authors: Santiago Balseiro, Max Lin, Vahab Mirrokni, Renato Leme, IIIS Song Zuo
NeurIPS 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically evaluate our revenue sharing scheme on real data. |
| Researcher Affiliation | Collaboration | Santiago Balseiro Columbia University New York City, NY EMAIL Max Lin Google New York City, NY EMAIL Vahab Mirrokni Google New York City, NY EMAIL Renato Paes Leme Google New York City, NY EMAIL Song Zuo Tsinghua University Beijing, China EMAIL |
| Pseudocode | Yes | ALGORITHM 1: Heuristic Refund Policy from Lagrangian Relaxation |
| Open Source Code | No | The paper does not provide a specific link or explicit statement for the availability of its source code. |
| Open Datasets | No | The paper states: "Our data set will consist of a random sample of auctions from 20 large publishers over the period of 2 days." However, it does not provide concrete access information (link, DOI, or specific citation) for this dataset to be publicly available. |
| Dataset Splits | Yes | Our data set will consist of a random sample of auctions from 20 large publishers over the period of 2 days. We will partition the data set in a training set consisting of data for the first day and a testing set consisting of data for the second day. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions preprocessing steps to learn parameters but does not provide specific hyperparameter values or detailed system-level training settings for experimental setup. |