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..
PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference
Authors: Xutao Wang, Hanting Chen, Tianyu Guo, Yunhe Wang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on three benchmark datasets demonstrate the proposed PUe algorithm significantly improves the accuracy of classifiers on non-uniform label distribution datasets compared to advanced cost-sensitive PU methods. Codes are available at https://github.com/huawei-noah/Noah-research/ tree/master/PUe and https://gitee.com/mindspore/models/ tree/master/research/cv/PUe. |
| Researcher Affiliation | Industry | Xutao Wang, Hanting Chen, Tianyu Guo, Yunhe Wang Huawei Noah s Ark Lab. EMAIL, |
| Pseudocode | Yes | Algorithm 1 PUe algorithm |
| Open Source Code | Yes | Codes are available at https://github.com/huawei-noah/Noah-research/ tree/master/PUe and https://gitee.com/mindspore/models/ tree/master/research/cv/PUe. |
| Open Datasets | Yes | We conducted experiments on two benchmarks commonly used in PU learning: MNIST for parity classification and CIFAR-10 [17] for vehicle class recognition. And on the simulated datasets of MNIST and CIFAR-10, we know the propensity score of the sample a priori, and we compare our proposed method with the ideal propensity to know the propensity score.Moreover, we tested our method on the Alzheimer s dataset 2 used to identify Alzheimer s disease in order to test the performance of our proposed method in real-world scenarios. |
| Dataset Splits | No | The paper mentions training, testing, and evaluation metrics but does not explicitly detail validation splits. It does specify 'warm-up phase of 60 epochs, and then trains another 60 epochs with depth' which implies some form of training/validation cycle, but no specific validation set details. |
| Hardware Specification | No | The paper mentions "CANN (Compute Architecture for Neural Networks) and Ascend AI Processor" but without specific model numbers or detailed specifications of the processors, it's not a reproducible hardware description. |
| Software Dependencies | No | The paper mentions "All the experiments are run by Py Torch" and acknowledges "Mind Spore [20]", but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The training batch size is set as 256 for MNIST and CIFAR10, while 128 for Alzheimer. We use Adam as the optimizer with a cosine annealing scheduler, where the initial learning rate is set as 5 10 3; while weight decay is set as 5 10 3. PU learning methods first experiences a warm-up phase of 60 epochs, and then trains another 60 epochs with depth, where the value of α is searched in the range of [0, 20]. |