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 from Multiple Unlabeled Datasets
Authors: Zheng Xie, Yu Liu, Ming Li
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we report the experimental results of the proposed Um-AUC, compared to state-of-the-art Um classification approaches. |
| Researcher Affiliation | Academia | National Key Laboratory for Novel Software Technology, Nanjing University, China School of Artificial Intelligence, Nanjing University, China |
| Pseudocode | Yes | Algorithm 1 Um-AUC |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a direct link to a code repository. |
| Open Datasets | Yes | We tested the performance of Um-AUC using the benchmark datasets Kuzushiji-MNIST (K-MNIST for short) (Clanuwat et al. 2018), CIFAR-10, and CIFAR100 (Krizhevsky, Hinton et al. 2009) |
| Dataset Splits | No | The paper mentions 'training set' and 'test set' but does not explicitly provide details about a validation set or specific training/validation splits. It states: 'We train all models for 150 epochs, and report the AUC on the test set at the final epoch.' |
| Hardware Specification | Yes | Our implementation is based on Py Torch (Paszke et al. 2019), and experiments are conducted on an NVIDIA Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al. 2019)' but does not specify a version number for it or any other software dependencies needed for replication. |
| Experiment Setup | Yes | We train all models for 150 epochs, and report the AUC on the test set at the final epoch. We used Adam (Kingma and Ba 2014) and cross-entropy loss for their optimization, following the standard implementation in the original paper. |