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..
A Practical Semi-Parametric Contextual Bandit
Authors: Yi Peng, Miao Xie, Jiahao Liu, Xuying Meng, Nan Li, Cheng Yang, Tao Yao, Rong Jin
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthetic data as well as a real dataset from one of the largest e-commercial platforms demonstrate the superior performance of our algorithm. |
| Researcher Affiliation | Collaboration | Yi Peng1 , Miao Xie1 , Jiahao Liu1 , Xuying Meng2 , Nan Li1 , Cheng Yang1 , Tao Yao1 and Rong Jin1 1Alibaba Group, Hang Zhou, China 2Institute of Computing Technology, Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1 SPUCB |
| Open Source Code | No | The paper does not provide concrete access to source code. It mentions "Our method has also been deployed as a service to support online businesses in Alibaba." but no public release. |
| Open Datasets | No | The synthetic dataset is randomly generated following our assumptions." and "The real-world dataset is collected from one of the largest ecommercial platform in China for the problem of products recommendation." Neither is publicly available with access info. |
| Dataset Splits | No | The paper mentions "All super-parameters of the above algorithms are tuned by a cross-validation experiment with the best performance" but does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) for the main experiments. |
| Hardware Specification | No | "The total running time for online updating parameters is less than 20ms with a single machine with 4-core CPUs." This describes the CPU core count but lacks specific hardware model details like CPU model, GPU model, or memory. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | All super-parameters of the above algorithms are tuned by a cross-validation experiment with the best performance. (Specifically, UCB: c = 0.2, LINUCB: α = 3.5, LSPS: σ1 = 0.3, σ2 = 0.01, σ3 = 0.3, SPUCB: R = 0.2, δ = 0.9, λ = 1.0, Rr = 0.5) |