Optimal Gradient-based Algorithms for Non-concave Bandit Optimization
Authors: Baihe Huang, Kaixuan Huang, Sham Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we design stochastic zeroth-order gradient-like2 ascent algorithms to attain minimax regret for a large class of structured polynomials. ... If you ran experiments... [N/A] |
| Researcher Affiliation | Collaboration | 1Peking University 2Princeton University 3University of Harvard 4Microsoft Research 5Tsinghua University |
| Pseudocode | Yes | Algorithm 1 Noisy power method for bandit eigenvalue problem. ... As presented in Algorithm 4, we conduct noisy subspace iteration... More details are deferred to Algorithm 5 and the Appendix. We show the bandit optimization procedure in Algorithm 6 |
| Open Source Code | No | The paper is theoretical and does not mention releasing source code. The questionnaire in the paper explicitly states 'N/A' for code availability in the 'If you ran experiments' section. |
| Open Datasets | No | This is a theoretical paper that defines abstract 'action sets' and 'function classes' but does not utilize or refer to specific publicly available datasets for experimental training. |
| Dataset Splits | No | This is a theoretical paper and does not involve experimental validation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not report on experimental hardware specifications. The 'If you ran experiments...' section of the self-evaluation checklist explicitly marks hardware details as 'N/A'. |
| Software Dependencies | No | The paper is theoretical and does not report on software dependencies for experimental replication. The 'If you ran experiments...' section of the self-evaluation checklist explicitly marks software details as 'N/A'. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, hyperparameters, or system-level training settings. The 'If you ran experiments...' section of the self-evaluation checklist explicitly marks these details as 'N/A'. |