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..
Thompson Sampling Algorithms for Mean-Variance Bandits
Authors: Qiuyu Zhu, Vincent Tan
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive set of simulations: We provide extensive sets of simulations for both Gaussian and Bernoulli bandits to show that our algorithms outperform state-of-the-art algorithms for mean-variance bandits. |
| Researcher Affiliation | Academia | 1Institute of Operations Research and Analytics, National University of Singapore, Singapore 2Department of Electrical and Computer Engineering, National University of Singapore, Singapore 3Department of Mathematics, National University of Singapore, Singapore. |
| Pseudocode | Yes | Algorithm 1 Update (ˆµi,t 1, Ti,t 1, αi,t 1, βi,t 1) ... Algorithm 2 Thompson Sampling for Mean Learning (MTS) and Variance Learning (VTS) ... Algorithm 3 Thompson Sampling for Gaussian mean-variance bandits (MVTS) ... Algorithm 4 Thompson Sampling for Bernoulli mean-variance bandits (BMVTS) |
| Open Source Code | Yes | The R code for all our experiments is provided along with this submission. |
| Open Datasets | Yes | The K = 15 Gaussian arms are set to the same as the experiments from Sani et al. (2012) (i.e. µ = (0.1, 0.2, 0.23, 0.27, 0.32, 0.32, 0.34, 0.41, 0.43, 0.54, 0.55, 0.56, 0.67, 0.71, 0.79), σ2 i = (0.05, 0.34, 0.28, 0.09, 0.23, 0.72, 0.19, 0.14, 0.44, 0.53, 0.24, 0.36, 0.56, 0.49, 0.85)). |
| Dataset Splits | No | The paper discusses the time horizon and number of runs for simulations ('The time horizon n = 30, 000 is fixed and the regret is averaged over 500 runs.') but does not specify explicit training, validation, or test dataset splits or cross-validation methodology. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'The R code for all our experiments is provided along with this submission.' but does not specify the version of R or any specific R packages/libraries with their version numbers. |
| Experiment Setup | Yes | The K = 15 Gaussian arms are set to the same as the experiments from Sani et al. (2012) (i.e. µ = (0.1, 0.2, 0.23, 0.27, 0.32, 0.32, 0.34, 0.41, 0.43, 0.54, 0.55, 0.56, 0.67, 0.71, 0.79), σ2 i = (0.05, 0.34, 0.28, 0.09, 0.23, 0.72, 0.19, 0.14, 0.44, 0.53, 0.24, 0.36, 0.56, 0.49, 0.85)). The time horizon n = 30, 000 is fixed and the regret is averaged over 500 runs. |