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..
Symmetric Linear Bandits with Hidden Symmetry
Authors: Phuong Nam Tran, The Anh Ta, Debmalya Mandal, Long Tran-Thanh
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To illustrate the performance of our algorithm, we conduct simulations where the entries of θ satisfy three cases: sparsity, non-crossing partitions and non-nesting partitions. |
| Researcher Affiliation | Academia | Nam Phuong Tran Department of Computer Science University of Warwick Coventry, United Kingdom EMAIL The Anh Ta CSIRO s Data61 Marsfield, NSW, Australia EMAIL Debmalya Mandal Department of Computer Science University of Warwick Coventry, United Kingdom EMAIL Long Tran-Thanh Department of Computer Science University of Warwick Coventry, United Kingdom EMAIL |
| Pseudocode | Yes | Algorithm 1 Explore Models then Commit |
| Open Source Code | Yes | Code is available at: https://github.com/Nam Tran Kek L/Symmetric-Linear-Bandit-with-Hidden-Symmetry.git. |
| Open Datasets | No | The paper uses synthetic data generated for its simulations and does not provide access information for a pre-existing public dataset. |
| Dataset Splits | No | The paper describes an exploration phase followed by a commitment phase, but does not explicitly mention separate training/validation/test dataset splits or cross-validation. |
| Hardware Specification | No | The paper describes its simulations and discusses computational complexity, but does not provide specific details about the hardware (e.g., CPU/GPU models, memory) used to run these experiments. |
| Software Dependencies | No | The paper refers to algorithms and techniques (e.g., Lasso regression, OFUL algorithm) but does not list specific software packages or libraries with version numbers required to replicate the experiments. |
| Experiment Setup | Yes | The set of arms X is d Sd 1, σ = 0.1, and (d, d0) {(40, 4), (80, 10), (100, 15)}. We let exploratory distribution ν be the uniform distribution on the unit sphere. The ground-truth partition πG and θ are randomized before each simulation. |