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..
Non-Stationary Dueling Bandits Under a Weighted Borda Criterion
Authors: Joe Suk, Arpit Agarwal
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We establish the first optimal and adaptive dynamic regret upper bound O( L1/3K1/3T 2/3), where L is the unknown number of significant Borda winner switches. We also introduce a novel weighted Borda score framework which generalizes both the Borda and Condorcet problems. |
| Researcher Affiliation | Academia | Joe Suk EMAIL Columbia University Arpit Agarwal EMAIL Columbia University |
| Pseudocode | Yes | Algorithm 1: BOSSE(tstart, m0): (Weighted) BOrda Score Soft Elimination ... Algorithm 2: Meta-BOSSE |
| Open Source Code | No | Future work can also implement Algorithm 2 on real datasets and compare with other algorithms for nonstationary dueling bandits. |
| Open Datasets | No | The paper describes a theoretical framework for K-armed dueling bandits with a pairwise mean preference matrix and does not refer to any specific datasets for experimental evaluation. |
| Dataset Splits | No | The paper is theoretical and does not use any specific datasets for empirical evaluation, therefore no dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware specifications. |
| 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 with specific hyperparameters or training configurations. |