Symmetric Linear Bandits with Hidden Symmetry
Authors: Phuong Nam Tran, The Anh Ta, Debmalya Mandal, Long Tran-Thanh
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 nam.p.tran@warwick.ac.uk The Anh Ta CSIRO s Data61 Marsfield, NSW, Australia theanh.ta@csiro.au Debmalya Mandal Department of Computer Science University of Warwick Coventry, United Kingdom debmalya.mandal@warwick.ac.uk Long Tran-Thanh Department of Computer Science University of Warwick Coventry, United Kingdom long.tran-thanh@warwick.ac.uk |
| 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. |