Combinatorial Pure Exploration of Multi-Armed Bandits
Authors: Shouyuan Chen, Tian Lin, Irwin King, Michael R Lyu, Wei Chen
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | 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 Confidence 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. |