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..
Bandits with many optimal arms
Authors: Rianne de Heide, James Cheshire, Pierre Ménard, Alexandra Carpentier
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Numerical Experiments We compare our algorithm against other standard bandit algorithms via numerical simulations. The results reported are averaged over 100 independent trials. |
| Researcher Affiliation | Collaboration | Alon Cohen: Massachusetts Institute of Technology, Cambridge, MA, USA, EMAIL. Yishay Mansour: Google and Tel Aviv University, Tel Aviv, Israel, EMAIL. |
| Pseudocode | No | The paper describes the algorithm mathematically and textually, but does not include a dedicated 'Pseudocode' or 'Algorithm' block with structured steps. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide a link to a code repository for the described methodology. |
| Open Datasets | No | The paper conducts numerical simulations rather than using a publicly available dataset. It describes the simulation setup (e.g., '100 independent trials', 'Gaussian distribution with mean µ and variance σ2 = 0.01'). There is no external dataset to provide access information for. |
| Dataset Splits | No | The paper discusses numerical simulations and hyperparameter tuning but does not explicitly provide details on train/validation/test dataset splits, as is common in supervised learning. It mentions: 'The hyperparameter C was selected to achieve the best empirical performance on the range of parameters we tested.' |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the numerical experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | The paper provides details on the experimental setup, including the number of trials ('averaged over 100 independent trials'), the horizon ('T = 10000'), number of arms ('K = 100'), and the distribution parameters for rewards ('Gaussian distribution with mean µ and variance σ2 = 0.01'). It also mentions that 'The hyperparameter C was selected to achieve the best empirical performance on the range of parameters we tested.' |