Stochastic Online AUC Maximization
Authors: Yiming Ying, Longyin Wen, Siwei Lyu
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We establish theoretical convergence of SOLAM with high probability and demonstrate its effectiveness on standard benchmark datasets. In this section, we report experimental evaluations of the SOLAM algorithm and comparing its performance with existing state-of-the-art learning algorithms for AUC maximization. |
| Researcher Affiliation | Academia | Department of Mathematics and Statistics SUNY at Albany, Albany, NY, 12222, USA Department of Computer Science SUNY at Albany, Albany, NY, 12222, USA |
| Pseudocode | Yes | Table 1: Pseudo code of the proposed algorithm. |
| Open Source Code | No | The paper does not provide a link to open-source code or explicitly state its release. |
| Open Datasets | Yes | Information about these datasets is summarized in Table 2. Table 2 lists datasets such as diabetes, fourclass, german, splice, usps, a9a, mnist, acoustic, ijcnn1, covtype, sector, and news20, which are standard benchmark datasets. |
| Dataset Splits | Yes | In the training phase, we use five-fold cross validation to determine the initial learning rate ζ [1 : 9 : 100] and the bound on w, R 10[ 1:1:5] by a grid search. |
| Hardware Specification | Yes | Experiments were performed with running time reported based on a workstation with 12 nodes, each with an Intel Xeon E5-2620 2.0GHz CPU and 64GB RAM. |
| Software Dependencies | No | SOLAM was implemented in MATLAB, and MATLAB code of the compared methods were obtained from the authors of corresponding papers. No specific version numbers for MATLAB or other software dependencies are provided. |
| Experiment Setup | No | The paper mentions a grid search for learning rate and a bound on w, but does not provide specific hyperparameter values, training configurations, or system-level settings in detail. |