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..
Semi-Parametric Sampling for Stochastic Bandits with Many Arms
Authors: Mingdong Ou, Nan Li, Cheng Yang, Shenghuo Zhu, Rong Jin7933-7940
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Also, experiments demonstrate the superiority of the proposed approach. |
| Researcher Affiliation | Industry | Mingdong Ou, Nan Li, Cheng Yang, Shenghuo Zhu, Rong Jin Alibaba Group, Hang Zhou, China EMAIL |
| Pseudocode | Yes | Algorithm 1 Semi-Parametric Sampling; Algorithm 2 Linear Semi-Parametric Sampling |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The synthetic data is randomly generated. The e-commerce dataset is collected from an online e-commerce platform. No concrete access information (link, DOI, citation with authors/year) is provided for any dataset. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The distribution of stochastic reward, expected reward and linear parameter are all implemented by Gaussian distribution. Specifically, rt|γit N(γit, σ2 1) , γi|θ N(θ xi, σ2 2) , θ N(0, σ2 3I) , where σ1, σ2 and σ3 are all hyper-parameters. |