Robust Advertisement Allocation
Authors: Shaojie Tang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run the simulations for each set of candidate models multiple rounds and report the worst-case approximation ratio in Figure 1. As shown in the figure, the approximation ratio stays above 0.71 across all the test cases, which empirically demonstrates the effectiveness of our algorithm. |
| Researcher Affiliation | Academia | Shaojie Tang Naveen Jindal School of Management University of Texas at Dallas shaojie.tang@utdallas.edu |
| Pseudocode | Yes | Algorithm 1 Double Oracle for Robust Advertising |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology described. |
| Open Datasets | No | The paper states 'We generate multiple sets of candidate click-through models as follows.' and describes the generation process, but does not provide concrete access information (link, DOI, repository, or citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes how data was generated ('We generate multiple sets of candidate click-through models as follows.'), but it does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology like train/validation/test splits or cross-validation). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For each candidate model θ Θ, we set the number of ads n = 100, the number of slates L = 3, the number of ad slots in each slate ms = 5, the continuation probability of each ad cθ ai = 0.9. The click through probability of each ad, qθ ai, is randomly sampled from [0, 1], and the revenue of clicking each ad is randomly selected from [1, 10]. A slate sequence, πθ, is randomly generated for each candidate model. |