Variance-Aware Sparse Linear Bandits
Authors: Yan Dai, Ruosong Wang, Simon Shaolei Du
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we present the first variance-aware regret guarantee for sparse linear bandits: e O q d PT t=1 σ2 t + 1 , where σ2 t is the variance of the noise at the t-th round. This bound naturally interpolates the regret bounds for the worst-case constant-variance regime (i.e., σt Ω(1)) and the benign deterministic regimes (i.e., σt 0). To achieve this variance-aware regret guarantee, we develop a general framework that converts any variance-aware linear bandit algorithm to a variance-aware algorithm for sparse linear bandits in a black-box manner. |
| Researcher Affiliation | Academia | Yan Dai IIIS, Tsinghua University, Ruosong Wang University of Washington, Simon S. Du University of Washington |
| Pseudocode | Yes | Our framework VASLB is presented in Algorithm 1. |
| Open Source Code | No | The paper does not provide an unambiguous statement or link indicating the release of source code for the described methodology. |
| Open Datasets | No | The paper describes theoretical work and does not use or refer to any specific dataset for training or evaluation. |
| Dataset Splits | No | The paper presents theoretical analysis and does not involve experimental validation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers (e.g., libraries, frameworks, or solvers) used for running experiments. |
| Experiment Setup | No | The paper focuses on theoretical development and analysis of algorithms, thus it does not provide details of an experimental setup such as hyperparameters or training configurations. |