On the Efficiency and Equilibria of Rich Ads
Authors: MohammadAmin Ghiasi, MohammadTaghi Hajiaghayi, Sébastien Lahaie, Hadi Yami
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We design and analyze a constant-factor approximation algorithm for the efficient allocation problem under fractionally subadditive CTRs, and a log-approximation algorithm for the subadditive case. Building on these results, we show that approximate competitive equilibrium prices exist and can be computed for subadditive and fractionally subadditive CTRs, with the same guarantees as for allocation. |
| Researcher Affiliation | Collaboration | 1University of Maryland 2Google Research |
| Pseudocode | Yes | Algorithm 1: Consecutive Welfare Maximization with XOS Valuations; Algorithm 2: Consecutive Welfare Maximization with Subadditive Valuations; Algorithm 3: Efficient Envy-free Pricing Method |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus it does not mention public datasets or provide access information for them. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments or dataset evaluation, therefore, it does not discuss training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus there is no mention of hardware specifications used for running experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and analysis, not empirical implementation details. Therefore, it does not list software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup, thus there are no details about hyperparameters or system-level training settings. |