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].
Revenue Maximization for Finitely Repeated Ad Auctions
Authors: Jiang Rong, Tao Qin, Bo An, Tie-Yan Liu
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our approach can significantly improve the revenue for the auctioneer in finitely repeated ad auctions. We evaluate the performance of our proposed approach using numerical simulations. The experimental results show significant improvements in revenue for the auctioneer as compared with baseline strategies. |
| Researcher Affiliation | Collaboration | Jiang Rong,1 The Key Lab of Intelligent Information Processing, ICT, CAS University of Chinese Academy of Sciences, Beijing 100190, China EMAIL 2Microsoft Research, Beijing 100080, China EMAIL 3School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798 EMAIL |
| Pseudocode | Yes | Algorithm 1: Optimal sample size |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of its source code. |
| Open Datasets | No | The paper states: "We use the commonly used log normal distribution and exponential distribution to evaluate the performance of Algorithm 1." However, it does not provide concrete access information such as a link, DOI, repository, or formal citation to a specific dataset or tool for generating samples from these distributions. |
| Dataset Splits | No | The paper describes a simulation process with sampling rounds and deployment rounds but does not specify traditional training/validation/test dataset splits in terms of percentages, sample counts, or predefined citations. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The parameters for the former distribution are set as μ = 0, σ = 1.5 and the corresponding optimal reserve price r is 4.2755. The parameter for the latter is λ = 3, with which we have that r = 3.0005. R( ) for each setting (N = 5) is averaged over 400 instances. |