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..
Choice Bandits
Authors: Arpit Agarwal, Nicholas Johnson, Shivani Agarwal
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that for the special case of k = 2, our algorithm is competitive with previous dueling bandit algorithms, and for the more general case k > 2, outperforms the recently proposed Max Min UCB algorithm designed for the MNL model. |
| Researcher Affiliation | Academia | Arpit Agarwal University of Pennsylvania Philadelphia, PA 19104, USA EMAIL Nicholas Johnson University of Minnesota Minneapolis, MN 55455, USA EMAIL Shivani Agarwal University of Pennsylvania Philadelphia, PA 19104, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Winner Beats All (WBA) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | Sushi: Choice model extracted from the Sushi dataset [47]; |
| Dataset Splits | No | The paper does not specify dataset splits for training, validation, or testing, nor does it mention cross-validation. It refers generally to 'datasets' for experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | The parameter C in our algorithm was set to 1. ... We set α = 0.51 for RUCB and DTS, and f(K) = 0.3K1.01 for RMED, and γ = 1.3 for BTM. ... We set the parameter α to be 0.51 for Max Min UCB. |