No Regrets for Learning the Prior in Bandits
Authors: Soumya Basu, Branislav Kveton, Manzil Zaheer, Csaba Szepesvari
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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. |