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..
Verification Based Solution for Structured MAB Problems
Authors: Zohar S. Karnin
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We consider the problem of finding the best arm in a stochastic Multi-armed Bandit (MAB) game and propose a general framework based on verification that applies to multiple well-motivated generalizations of the classic MAB problem. Our results are focused on the scenario where the failure probability δ must be very low; we essentially show that in this high confidence regime, identifying the best arm is as easy as the task of verification. |
| Researcher Affiliation | Industry | Zohar Karnin Yahoo Research New York, NY 10036 EMAIL |
| Pseudocode | Yes | Algorithm 1 Explore-Verify Framework |
| Open Source Code | No | The paper is theoretical and does not mention the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on algorithmic frameworks and complexity analysis, not empirical evaluation. It does not mention or provide access information for any publicly available or open datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments involving dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup, such as hyperparameters or system-level training settings. |