Bandit Multi-linear DR-Submodular Maximization and Its Applications on Adversarial Submodular Bandits
Authors: Zongqi Wan, Jialin Zhang, Wei Chen, Xiaoming Sun, Zhijie Zhang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate the online bandit learning of the monotone multi-linear DR-submodular functions, designing the algorithm Bandit MLSM that attains O(T 2/3 log T) of (1 1/e)-regret. Then we reduce submodular bandit with partition matroid constraint and bandit sequential monotone maximization to the online bandit learning of the monotone multi-linear DR-submodular functions, attaining O(T 2/3 log T) of (1 1/e)-regret in both problems, which improve the existing results. |
| Researcher Affiliation | Collaboration | 1Institute of Computing Technology, Chinese Academy of Sciences 2University of Chinese Academy of Sciences 3Microsoft Research 4Center for Applied Mathematics of Fujian Province, School of Mathematics and Statistics, Fuzhou University. |
| Pseudocode | Yes | Algorithm 1 Bandit MLSM(η, L, Φ) [...] Algorithm 2 MLSMWrapper(η, L, Φ, EXT) [...] Algorithm 3 Bandit DRSM(η, δ, L, Φ) [...] Algorithm 4 Bandit MLSM4PS(η, L, Φ) |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper is a theoretical work focusing on algorithms and regret bounds, and does not use or mention any specific publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, thus no dataset split information for validation is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and proofs. It does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not report on empirical experiments, thus no details on experimental setup like hyperparameters or training settings are provided. |