Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
How Does Variance Shape the Regret in Contextual Bandits?
Authors: Zeyu Jia, Jian Qian, Alexander Rakhlin, Chen-Yu Wei
NeurIPS 2024 | Venue PDF | 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 EMAIL Jian Qian Massachusetts Institute of Technology EMAIL Alexander Rakhlin Massachusetts Institute of Technology EMAIL Chen-Yu Wei University of Virginia EMAIL |
| 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. |