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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Robust Advertisement Allocation
Authors: Shaojie Tang
IJCAI 2017 | Venue PDF | 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 EMAIL |
| 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. |