Spectral Thompson Sampling
Authors: Tomáš Kocák, Michal Valko, Rémi Munos, Shipra Agrawal
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also show that our algorithm is competitive on both synthetic and real-world data. |
| Researcher Affiliation | Collaboration | Tom aˇs Koc ak Seque L team INRIA Lille Nord Europe France Michal Valko Seque L team INRIA Lille Nord Europe France R emi Munos Seque L team INRIA Lille, France Microsoft Research NE, USA Shipra Agrawal ML and Optimization Group Microsoft Research Bangalore, India |
| Pseudocode | Yes | Algorithm 1 Spectral Thompson Sampling |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Furthermore, we performed the comparison of the algorithms on the Movie Lens dataset (Lam and Herlocker 2012) of the movie ratings. |
| Dataset Splits | No | The paper mentions using synthetic and real-world data but does not provide specific training/validation/test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running the experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions) needed to replicate the experiment. |
| Experiment Setup | Yes | In all experiments, we had δ = 0.001, λ = 1, and R = 0.01. |