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..
Towards Decision-Friendly AUC: Learning Multi-Classifier with AUCµ
Authors: Peifeng Gao, Qianqian Xu, Peisong Wen, Huiyang Shao, Yuan He, Qingming Huang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on four benchmark datasets demonstrate the effectiveness of our proposed method in both AUCµ and F1metric. |
| Researcher Affiliation | Collaboration | Peifeng Gao 1, Qianqian Xu 2, *, Peisong Wen 1, 2 Huiyang Shao 1, 2, Yuan He 3, Qingming Huang 1, 2, 4, 5, 1 School of Computer Science and Tech., University of Chinese Academy of Sciences 2 Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences 3 Alibaba Group 4 BDKM, University of Chinese Academy of Sciences 5 Peng Cheng Laboratory EMAIL, EMAIL EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | To demonstrate the effectiveness of our proposed framework, we conduct a series of experiments in four benchmark datasets for imbalanced multi-classification: CIFAR10, CIFAR100 (Krizhevsky 2012), Tiny Image Net (Russakovsky et al. 2015) and Image Net (Deng et al. 2009). |
| Dataset Splits | Yes | We keep the models with the highest AUCµ in the validation set and report the corresponding AUCµ and F1-metric on the test set. The training epochs are set to 25 for Image Net and 80 for other datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We utilize the Adam optimizer (Kingma and Ba 2017) for all methods. The initial learning rates are searched in [10 4, 10 3], and decays by 0.99 per epoch. We keep the models with the highest AUCµ in the validation set and report the corresponding AUCµ and F1-metric on the test set. The training epochs are set to 25 for Image Net and 80 for other datasets. |