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..
Categorized Bandits
Authors: Matthieu Jedor, Vianney Perchet, Jonathan Louedec
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we have conducted an analysis on real data to highlight that those ordered categories actually exist in practice. Finally, we conduct numerical experiments on different scenarios to illustrate both finite-time and asymptotic performances of our algorithms compared to algorithms either agnostic to the structure or only taking it partly into account. |
| Researcher Affiliation | Collaboration | Matthieu Jedor CMLA, ENS Paris-Saclay & Cdiscount EMAIL Jonathan Louëdec Cdiscount EMAIL Vianney Perchet CMLA, ENS Paris-Saclay & Criteo AI Lab EMAIL |
| Pseudocode | Yes | Algorithm 1: CATSE(δ) |
| Open Source Code | No | The paper does not provide concrete access to source code for the described methodology. No links to repositories or explicit statements of code release are found. |
| Open Datasets | No | We have collected the CTR of products in four different categories over one month on the e-commerce website Cdiscount, one of the leading ecommerce companies in France, gathered in Table 2a. For privacy reason, the exact content of the different categories cannot be revealed. |
| Dataset Splits | No | The paper describes numerical experiments where rewards are drawn from Gaussian distributions or analysis of collected real-world data (for which privacy is cited as a reason for non-disclosure), but does not specify any training/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. It only states that simulations were run. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions) needed to replicate the experiment. |
| Experiment Setup | Yes | The simulations were ran until time horizon 10,000 and results were averaged over 100 independent runs. CATSEs and CATSE0 were run with δ = 1/t and δ = 1/Mt, respectively. |