PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference
Authors: Xutao Wang, Hanting Chen, Tianyu Guo, Yunhe Wang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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. {xutao.wang,chenhanting,tianyu.guo,yunhe.wang}@huawei.com, |
| 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]. |