Scalable Discrete Sampling as a Multi-Armed Bandit Problem
Authors: Yutian Chen, Zoubin Ghahramani
ICML 2016 | Conference 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 YUTIAN.CHEN@ENG.CAM.AC.UK Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK Zoubin Ghahramani ZOUBIN@ENG.CAM.AC.UK 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. |