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..
Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits
Authors: Aadirupa Saha, Shubham Gupta
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive simulations corroborate our results. |
| Researcher Affiliation | Industry | 1Microsoft Research, New York City, United States. 2IBM Research, Orsay, France. |
| Pseudocode | Yes | Algorithm 1 presents the pseudocode for DEX3.P. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | No | We simulate an environment where these values follow a Gaussian random walk. That is, for every t ∈ [T] and i < j, Pt+1(i, j) = Pt(i, j) + ϵt(i, j), where ϵt(i, j) ∼ N(0, 0.002). ... The initial values P1(i, j) ∼ Uniform(0, 1). |
| Dataset Splits | No | The paper describes generating synthetic data for simulations but does not specify distinct training, validation, and test dataset splits. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | The values of parameters α, β, η, and γ for DEX3.P and DEX3.S were set in accordance with Theorems 3.3 and 4.1 (or 4.4 as appropriate from the context), respectively. ... We simulate an environment where these values follow a Gaussian random walk. That is, for every t ∈ [T] and i < j, Pt+1(i, j) = Pt(i, j) + ϵt(i, j), where ϵt(i, j) ∼ N(0, 0.002). |