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..
Optimal Best Markovian Arm Identification with Fixed Confidence
Authors: Vrettos Moulos
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We derive instance specific nonasymptotic and asymptotic lower bounds which generalize those of the IID setting. We analyze the Track-and-Stop strategy, initially proposed for the IID setting, and we prove that asymptotically it is at most a factor of four apart from the lower bound. Our one-parameter Markovian bandit model is based on the notion of an exponential family of stochastic matrices for which we establish many useful properties. For the analysis of the Track-and-Stop strategy we derive a novel concentration inequality for Markov chains that may be of interest in its own right. |
| Researcher Affiliation | Academia | Vrettos Moulos University of California Berkeley EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code (e.g., specific repository link, explicit code release statement) for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not mention using any specific datasets or providing access information for publicly available datasets for training purposes. |
| Dataset Splits | No | The paper is theoretical and does not provide specific dataset split information (percentages, sample counts, or splitting methodology) needed for reproduction, as it does not involve empirical validation. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, processor types) used for running experiments, as it is a theoretical work. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers), as it is a theoretical work without empirical implementation details. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (e.g., concrete hyperparameter values, training configurations, or system-level settings), as it is a theoretical work. |