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..
Robust Online Matching with User Arrival Distribution Drift
Authors: Yu-Hang Zhou, Chen Liang, Nan Li, Cheng Yang, Shenghuo Zhu, Rong Jin459-466
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a real-world dataset exhibit the superiority of our approach. |
| Researcher Affiliation | Industry | Yu-Hang Zhou, Chen Liang, Nan Li, Cheng Yang, Shenghuo Zhu, Rong Jin Alibaba Group, Hangzhou, China EMAIL |
| Pseudocode | Yes | Algorithm 1 One-Time Online Primal-Dual Algorithm and Algorithm 2 Robust Dynamic Learning Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The dataset used here consists of 245 bidders and millions of search queries from 7 days. Due to the consideration of trade secrets, all of the reported information about the dataset has been masked. |
| Dataset Splits | No | The paper defines ϵ as the 'fraction of users used for training dual variables' (e.g., fixed as 0.1), which implies a training set. It also mentions 'In practice, we could set this hyper-parameter via cross-validation' for δ, but does not explicitly describe a separate validation split or its percentage/counts for the reported experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | Parameters ϵ in RDLA, DLA-geometric, DLA-equal, and OT-PD are all fixed as 0.1, and λ in RDLA is set as 10. Furthermore, in order to see the influence of the choice of δ, we evaluate the proposed RDLA method with several δ setting, i.e., 1e 2, 1e 3, 1e 4. |