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..
Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction
Authors: Quanming Yao, Xiawei Guo, James Kwok, Weiwei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we not only demonstrate the effectiveness of our method on two benchmark data sets, i.e., MNIST and NEWS20, but also apply it into a real application of cross-organizational diabetes prediction from RUIJIN data set, where privacy is of a significant concern. |
| Researcher Affiliation | Collaboration | 14Paradigm Inc 2Department of Computer Science and Engineering, HKUST |
| Pseudocode | Yes | Algorithm 1 PLR: Privacy-preserving logistic regression. Algorithm 2 PST-S: Privacy-preserving stacking with SP. Algorithm 3 PST-F: Privacy-preserving stacking with FP. Algorithm 4 PST-H: Privacy-preserving stacking with HTL. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Experiments are performed on two popular benchmark data sets for evaluating privacy-preserving learning algorithms [Shokri and Shmatikov, 2015; Papernot et al., 2017; Wang et al., 2018]: MNIST [Le Cun et al., 1998] and NEWS20 [Lang, 1995] (Table 1). |
| Dataset Splits | Yes | 60% of them are used for training (with 1/3 of this used for validation), and the remaining 20% for testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, required to replicate the experiments. |
| Experiment Setup | Yes | We use K = 5 and 50% of the data for Dl and the remaining for Dh. We set ϵsrc = ϵtgt = 1.0. Hyper-parameters are tuned using the validation set. |