Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs
Authors: Han Shao, Xiaotian Yu, Irwin King, Michael R. Lyu
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our proposed algorithms are evaluated based on synthetic datasets, and outperform the state-of-the-art results. |
| Researcher Affiliation | Academia | Han Shao Xiaotian Yu Irwin King Michael R. Lyu Department of Computer Science and Engineering The Chinese University of Hong Kong {hshao,xtyu,king,lyu}@cse.cuhk.edu.hk |
| Pseudocode | Yes | Algorithm 1 Me dian of mean s under OFU; Algorithm 2 T runcation under OFU |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described algorithms (MENU and TOFU) is publicly available. |
| Open Datasets | No | To show effectiveness of bandit algorithms, we will demonstrate cumulative payoffs with respect to number of rounds for playing bandits over a fixed finite-arm decision set. For verifications, we adopt four synthetic datasets (named as S1 S4) in the experiments, of which statistics are shown in Table 1. |
| Dataset Splits | No | The paper mentions running experiments over a total number of rounds (T) and independent repetitions, but it does not describe specific train/validation/test dataset splits or cross-validation procedures for reproducibility. The data used is synthetic and generated for the experiments. |
| Hardware Specification | Yes | We run multiple independent repetitions for each dataset in a personal computer under Windows 7 with Intel CPU@3.70GHz and 16GB memory. |
| Software Dependencies | No | The paper mentions 'Windows 7' for the operating system, but does not specify any software dependencies with version numbers (e.g., programming languages, libraries, frameworks) crucial for reproducibility. |
| Experiment Setup | Yes | For all algorithms, we set λ = 1.0, and δ = 0.1. |