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..
Scalable and Efficient Pairwise Learning to Achieve Statistical Accuracy
Authors: Bin Gu, Zhouyuan Huo, Heng Huang3697-3704
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on a variety of real-world datasets not only confirm the effectiveness of our Ada DSG algorithm, but also show that Ada DSG has significantly better scalability and efficiency than the existing pairwise learning algorithms. |
| Researcher Affiliation | Collaboration | Bin Gu,1 Zhouyuan Huo,2 Heng Huang1,2 1JDDGlobal.com 2Department of Electrical & Computer Engineering, University of Pittsburgh, USA |
| Pseudocode | Yes | Algorithm 1 Adaptive doubly stochastic gradient algorithm (Ada DSG) ... Algorithm 2 DSGD algorithm |
| Open Source Code | No | We implemented our Ada DSG algorithm in MATLAB. (The paper mentions implementing its own algorithm but does not provide a link or explicit statement about making its source code available. It provides links to code for other algorithms used for comparison.) |
| Open Datasets | Yes | Table 3 summarizes the eight real-world benchmark datasets used in our experiments. They are the A9a, Covtype, Ijcnn1, Phishing, Usps, Mnist, Rcv1 and Real-sim datasets from the LIBSVM repository2. 2The LIBSVM repository is available at https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/. |
| Dataset Splits | No | We randomly partitioned each dataset into 75% for training and 25% for testing. (The paper specifies the training and testing splits but does not explicitly mention a validation dataset split or its size/percentage.) |
| Hardware Specification | Yes | Our experiments were performed on an 8-core Intel Xeon E3-1240 machine. |
| Software Dependencies | No | We implemented our Ada DSG algorithm in MATLAB. (The paper mentions MATLAB but does not provide a specific version number for MATLAB or any other software dependencies with version numbers.) |
| Experiment Setup | Yes | For our Ada DSG algorithm, the initial learning rate γ0 was tuned from 1 to 10 4, and the outer loop number was set as 20. In the implementation of our Ada DSG algorithm, we set Vn = 1 n, and set the inner loop number of DSGD for the subproblem Rm as m. |