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..
Variance-Aware Sparse Linear Bandits
Authors: Yan Dai, Ruosong Wang, Simon Shaolei Du
ICLR 2023 | Venue PDF | 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. |