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..
AUC Optimization with a Reject Option
Authors: Song-Qing Shen, Bin-Bin Yang, Wei Gao5684-5691
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We finally present extensive empirical studies to verify the effectiveness of the proposed algorithm. |
| Researcher Affiliation | Academia | National Key Laboratory for Novel Software Technology Nanjing University, Nanjing, 210023, China EMAIL |
| Pseudocode | Yes | Algorithm 1 The AUCRO Algorithm |
| Open Source Code | No | The paper does not provide explicit statements or links for open-source code for the described methodology. Footnote 1 points to a third-party tool. |
| Open Datasets | Yes | We evaluate the performance of our method on ten benchmark datasets, as summarized in Table 11. 1https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/ |
| Dataset Splits | Yes | Two trials of 5-fold cross-validation is executed on training sets to decide the learning rate ηt 2[ 12: 10] and the regularized parameter λ 2[ 10: 2] for our algorithm. |
| Hardware Specification | No | No specific hardware details (such as GPU or CPU models, or detailed system specifications) used for experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., libraries, frameworks, or solvers) are explicitly stated in the paper. |
| Experiment Setup | Yes | Two trials of 5-fold cross-validation is executed on training sets to decide the learning rate ηt 2[ 12: 10] and the regularized parameter λ 2[ 10: 2] for our algorithm. For FSAUC, we tune the initial stepsize η1 2[ 10: 10] and the parameter R 10[ 1: 5], as recommended in (Liu et al. 2018). For OPAUC, stepsize ηt is decided within the range 2[ 12: 10] and the regularization parameter λ is decided within the range 2[ 10: 2] as recommended in (Gao et al. 2016). For OAMseq and OAMgd, the buffer sizes are fixed to be 100 and the penalty parameter C is decided within 2[ 10: 10] as recommended in (Zhao et al. 2011). |