Simple Mechanisms for Welfare Maximization in Rich Advertising Auctions
Authors: Gagan Aggarwal, Kshipra Bhawalkar, Aranyak Mehta, Divyarthi Mohan, Alexandros Psomas
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we experimentally test our algorithms on real-world data. In this section we present some empirical results for our truthful mechanisms. |
| Researcher Affiliation | Collaboration | Gagan Aggarwal Google Research gagana@google.com Kshipra Bhawalkar Google Research kshipra@google.com Aranyak Mehta Google Research aranyak@google.com Divyarthi Mohan Tel Aviv University divyarthim@tau.ac.il Alexandros Psomas Purdue University apsomas@cs.purdue.edu |
| Pseudocode | Yes | Algorithm 1 (ALGI). First, run ALGB. Let Wi be the space allotted to advertiser i. Second, post-process to allocate the ad j with maximum value that fits in Wi, i.e. j 2 argmaxwij Wibij. Any remaining space is left unallocated. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] The data is proprietary. |
| Open Datasets | No | We evaluated our algorithms from real world data obtained from a large search engine. The data consists of a sample of approximately 11000 queries, selected to have at least 6 advertisers each. The assets are proprietary |
| Dataset Splits | No | The paper states it uses a sample of real-world data but does not provide specific training, validation, or test dataset splits. |
| Hardware Specification | No | We ran our algorithms on a single machine. This statement does not provide specific hardware details such as CPU/GPU models or memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We use 500 as the space limit as that is larger than the space of any individual rich ad. |