Online Bandit Learning for a Special Class of Non-Convex Losses
Authors: Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we investigate the problem of online bandit learning with non-convex losses, and develop an efficient algorithm with formal theoretical guarantees. Theoretical analysis shows that our algorithm achieves an e O(poly(d)T 2/3) regret bound when the variation of the loss function is small. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2Department of Computer Science, the University of Iowa, Iowa City, IA 52242, USA 3Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA |
| Pseudocode | Yes | Algorithm 1 An Efficient Algorithm for Online Bandit Learning |
| Open Source Code | No | The paper does not provide any specific links to source code repositories or explicit statements about code availability for the methodology described. |
| Open Datasets | No | This paper is theoretical and does not describe any empirical studies or the use of any specific datasets for training. |
| Dataset Splits | No | This paper is theoretical and does not describe any empirical studies or the use of any specific dataset splits for validation. |
| Hardware Specification | No | This paper is theoretical and does not describe any empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and does not describe any empirical experiments or specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and focuses on algorithm development and theoretical analysis, thus it does not provide details on experimental setup such as hyperparameters or training configurations. |