Community Exploration: From Offline Optimization to Online Learning
Authors: Xiaowei Chen, Weiran Huang, Wei Chen, John C. S. Lui
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a systematic study of the community exploration problem, from offline optimization to online learning. For the offline setting where the sizes of communities are known, we prove that the greedy methods for both of non-adaptive exploration and adaptive exploration are optimal. For the online setting where the sizes of communities are not known and need to be learned from the multi-round explorations, we propose an upper confidence like algorithm that achieves the logarithmic regret bounds. |
| Researcher Affiliation | Collaboration | 1The Chinese University of Hong Kong 2Huawei Noah s Ark Lab, 3Microsoft Research 1{xwchen, cslui}@cse.cuhk.edu.hk, 2huang.inbox@outlook.com 3weic@microsoft.com |
| Pseudocode | Yes | Algorithm 1 Non-Adaptive community exploration with optimal budget allocation; Algorithm 2 Adaptive community exploration with greedy policy; Algorithm 3 Combinatorial Lower Confidence Bound (CLCB) algorithm |
| Open Source Code | No | The paper does not mention providing open-source code for the methodology described. It is a theoretical paper focusing on proofs and algorithms. |
| Open Datasets | No | The paper is theoretical and does not describe experiments performed on a dataset, nor does it provide access information for any publicly available or open dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental data splits. Therefore, it does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithms. It does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate an experiment. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or system-level training settings. |