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..
Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs
Authors: Han Shao, Xiaotian Yu, Irwin King, Michael R. Lyu
NeurIPS 2018 | Venue PDF | 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 EMAIL |
| 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. |