How Does Variance Shape the Regret in Contextual Bandits?
Authors: Zeyu Jia, Jian Qian, Alexander Rakhlin, Chen-Yu Wei
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that a regret of Ω(..., we derive a nearly matching upper bound O(...), Theorem 4.1 (Main lower bound), Algorithm 1 Var CB (Variance-aware Contextual Bandits), The proof is provided in Appendix F. |
| Researcher Affiliation | Academia | Zeyu Jia Massachusetts Institute of Technology zyjia@mit.edu Jian Qian Massachusetts Institute of Technology jianqian@mit.edu Alexander Rakhlin Massachusetts Institute of Technology rakhlin@mit.edu Chen-Yu Wei University of Virginia chenyu.wei@virginia.edu |
| Pseudocode | Yes | Algorithm 1 Var CB (Variance-aware Contextual Bandits), Algorithm 2 Algorithm for Heteroscedastic Noise, Algorithm 3 Dist Var CB (Distributional Variance-aware Contextual Bandits), Algorithm 4 Prod-based online regression oracle, Algorithm 5 Var UCB, Algorithm 6 Variance Sensitive Square CB for Zero-One Noise |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology or links to code repositories. |
| Open Datasets | No | This paper is theoretical and does not use or reference any datasets for training or public access. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments requiring dataset splits. |
| Hardware Specification | No | This paper is theoretical and does not include any experimental results that would require a description of hardware specifications. |
| Software Dependencies | No | This paper is theoretical and does not describe any experiments that would require specific software dependencies or their version numbers. |
| Experiment Setup | No | This paper is theoretical and does not include an experimental setup, hyperparameters, or training configurations. |