Dynamic Revenue Sharing
Authors: Santiago Balseiro, Max Lin, Vahab Mirrokni, Renato Leme, IIIS Song Zuo
NeurIPS 2017 | Conference 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 srb2155@columbia.edu Max Lin Google New York City, NY whlin@google.com Vahab Mirrokni Google New York City, NY mirrokni@google.com Renato Paes Leme Google New York City, NY renatoppl@google.com Song Zuo Tsinghua University Beijing, China songzuo.z@gmail.com |
| 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. |