Semi-Parametric Sampling for Stochastic Bandits with Many Arms
Authors: Mingdong Ou, Nan Li, Cheng Yang, Shenghuo Zhu, Rong Jin7933-7940
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 {mingdong.omd, nanli.ln, charis.yangc, shenghuo.zhu, jinrong.jr}@alibaba-inc.com |
| 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. |