Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Optimal Best Markovian Arm Identification with Fixed Confidence
Authors: Vrettos Moulos
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 15,598 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,207 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |