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..
Combinatorial Pure Exploration of Multi-Armed Bandits
Authors: Shouyuan Chen, Tian Lin, Irwin King, Michael R Lyu, Wei Chen
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the combinatorial pure exploration (CPE) problem in the stochastic multi-armed bandit setting... We present general learning algorithms... We prove problem-dependent upper bounds of our algorithms. Our analysis exploits the combinatorial structures... We also establish a general problem-dependent lower bound for the CPE problem. |
| Researcher Affiliation | Collaboration | 1The Chinese University of Hong Kong 2Tsinghua University 3Microsoft Research Asia |
| Pseudocode | Yes | Algorithm 1 CLUCB: Combinatorial Lower-Upper Con๏ฌdence Bound; Algorithm 2 CSAR: Combinatorial Successive Accept Reject |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code for the described methodology is available. |
| Open Datasets | No | This is a theoretical paper that focuses on algorithm design and analysis, and it does not use or refer to any datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper that does not involve datasets or data splitting, thus no training, validation, or test splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments. Therefore, no hardware specifications are mentioned for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments requiring specific software dependencies or versions. It refers to conceptual 'oracles' rather than actual software. |
| Experiment Setup | No | The paper is theoretical and describes algorithms and their bounds, but it does not include details on an experimental setup, hyperparameters, or training configurations. |