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..
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis
Authors: Vidyashankar Sivakumar, Steven Wu, Arindam Banerjee
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose simple greedy algorithms for both the single- and multi-parameter (i.e., different parameter for each context) settings and provide a unified regret analysis for θ with any assumed structure. The regret bounds are expressed in terms of geometric quantities such as Gaussian widths associated with the structure of θ . We also obtain sharper regret bounds compared to earlier work for the unstructured θ setting as a consequence of our improved analysis. We show there is implicit exploration in the smoothed setting where a simple greedy algorithm works.3. Single Parameter Regret Analysis, Theorem 1 (Gaussian Contexts Regret Bounds), Theorem 2 (Smoothed Adversary Regret Bounds), 4. Multi Parameter Regret Analysis, Theorem 3 (Multi parameter Smoothed Adversary Regret Bounds). |
| Researcher Affiliation | Collaboration | 1Walmart Labs, Sunnyvale 2Department of Computer Science, University of Minnesota, Twin Cities. Correspondence to: Vidyashankar Sivakumar <EMAIL>, Zhiwei Steven Wu <EMAIL>, Arindam Banerjee <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Structured Greedy (single parameter) and Algorithm 2 High-dimensional Greedy (multi parameter). |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on regret analysis of linear contextual bandits. It does not mention the use of any specific public or open datasets for training or evaluation. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not perform experiments that would require explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical analysis and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or system-level training settings. |