Scalable Discrete Sampling as a Multi-Armed Bandit Problem
Authors: Yutian Chen, Zoubin Ghahramani
ICML 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations show the robustness and efficiency of the approximate algorithms in both synthetic and real-world large-scale problems.5. Experiments |
| Researcher Affiliation | Academia | Yutian Chen EMAIL Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK Zoubin Ghahramani EMAIL Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK Alan Turing Institute, 96 Euston Road, London NW1 2DB, UK |
| Pseudocode | Yes | Alg. 1 Racing Algorithm with a Finite Reward Population |
| Open Source Code | No | B provides a lookup table and a plot of BNormal(δ) = E 1(δ). Notice that BNormal only needs to be computed once and we can obtain it for any δ by either interpolating the table or computing numerically with code to be shared (runtime < 1 second). |
| Open Datasets | No | The paper mentions 'DBLP dataset' and 'Rexa corpus' but does not provide specific access information (link, DOI, repository, or formal citation for the dataset itself). |
| Dataset Splits | No | The paper does not provide specific training/test/validation dataset splits (e.g., percentages, sample counts, or explicit cross-validation setup). |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts) used for running experiments are mentioned. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) are provided. |
| Experiment Setup | Yes | We set m(1) = 50 and δ = 0.05. We use adjusted priors q as suggested by Carlin & Chib (1995) for sufficient mixing between all models and tune them with adaptive MCMC. |