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..
No Regrets for Learning the Prior in Bandits
Authors: Soumya Basu, Branislav Kveton, Manzil Zaheer, Csaba Szepesvari
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theory is supported by experiments, where Ada TS outperforms prior algorithms and works well even in challenging real-world problems. |
| Researcher Affiliation | Collaboration | Soumya Basu Google Branislav Kveton Google Research Manzil Zaheer Google Research Csaba Szepesvári Deep Mind / University of Alberta |
| Pseudocode | Yes | The pseudocode of our algorithm is in Algorithm 1. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code. |
| Open Datasets | No | The paper mentions using 'synthetic problems' and 'bandit classification problems' (implying 'digit 1' from Figure 3), but does not provide concrete access information (link, DOI, citation with authors/year) for any public dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) 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 | No | The paper states 'In both problems, the number of tasks is m = 20 and each task has n = 200 rounds', which provides some high-level experimental parameters, but lacks specific hyperparameter values, training configurations, or system-level settings typically expected for full reproducibility. |