Scalable and Efficient Pairwise Learning to Achieve Statistical Accuracy
Authors: Bin Gu, Zhouyuan Huo, Heng Huang3697-3704
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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. |