Thompson Sampling on Symmetric Alpha-Stable Bandits
Authors: Abhimanyu Dubey, Alex `Sandy' Pentland
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove finite-time regret bounds for both algorithms, and demonstrate through a series of experiments the stronger performance of Thompson Sampling in this setting. |
| Researcher Affiliation | Academia | Abhimanyu Dubey and Alex Sandy Pentland Massachusetts Institute of Technology {dubeya, pentland}@mit.edu |
| Pseudocode | Yes | Algorithm 1 Chambers-Mallows-Stuck Generation, Algorithm 2 α-Thompson Sampling, Algorithm 3 Robust α-Thompson Sampling |
| Open Source Code | No | The paper mentions a |
| Open Datasets | No | The paper conducts simulations and generates data for its experiments, rather than using a publicly available dataset with a specific link or citation. |
| Dataset Splits | No | The paper conducts simulations over a fixed number of iterations (T=5000 or T=15K) and evaluates regret over time, but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running experiments. It vaguely mentions |
| Software Dependencies | No | The paper does not provide specific software names with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | We run 100 MAB experiments each for all 5 benchmarks for α = 1.8 and α = 1.3, and K = 50 arms, and for each arm, the mean is drawn from [0, 2000] randomly for each experiment, and σ = 2500. Each experiment is run for T = 5000 iterations, and we report the regret averaged over time, i.e. R(t)/t at any time t. Also, |