Positive Unlabeled (PU) learning refers to the task of learning a binary
classifier given a few labeled positive samples, and a set of unlabeled samples
(which could be positive or negative). In this paper, we propose a novel PU
learning framework, that starts by learning a feature space through
pretext-invariant representation learning and then applies pseudo-labeling to
the unlabeled examples, leveraging the concentration property of the
embeddings. Overall, our proposed approach handily outperforms state-of-the-art
PU learning methods across several standard PU benchmark datasets, while not
requiring a-priori knowledge or estimate of class prior. Remarkably, our method
remains effective even when labeled data is scant, where most PU learning
algorithms falter. We also provide simple theoretical analysis motivating our
proposed algorithms and establish generalization guarantee for our approach.

我们提出了一种新的正未标化学习框架，通过预定义无关的表示学习来学习特征空间，然后采用伪标记技术将未标注样本进行分类，利用嵌入的集中性质。该方法在多个标准正未标化基准数据集上明显优于现有的方法，并且不需要先验知识或类先验的估计。值得注意的是，即使标注数据稀缺，我们的方法仍然有效，而大多数正未标化学习算法则表现不佳。我们还提供了简单的理论分析，解释了我们所提出算法的动机，并为我们的方法建立了泛化保证。

对无先验正样本无监督学习的对比方法

Contrastive Approach to Prior Free Positive Unlabeled Learning

Using unlabeled data to regularize the machine learning models has
demonstrated promise for improving safety and reliability in detecting
out-of-distribution (OOD) data. Harnessing the power of unlabeled in-the-wild
data is non-trivial due to the heterogeneity of both in-distribution (ID) and
OOD data. This lack of a clean set of OOD samples poses significant challenges
in learning an optimal OOD classifier. Currently, there is a lack of research
on formally understanding how unlabeled data helps OOD detection. This paper
bridges the gap by introducing a new learning framework SAL (Separate And
Learn) that offers both strong theoretical guarantees and empirical
effectiveness. The framework separates candidate outliers from the unlabeled
data and then trains an OOD classifier using the candidate outliers and the
labeled ID data. Theoretically, we provide rigorous error bounds from the lens
of separability and learnability, formally justifying the two components in our
algorithm. Our theory shows that SAL can separate the candidate outliers with
small error rates, which leads to a generalization guarantee for the learned
OOD classifier. Empirically, SAL achieves state-of-the-art performance on
common benchmarks, reinforcing our theoretical insights. Code is publicly
available at this https URL

使用无标签数据来规范机器学习模型已经显示出改善检测超出分布数据的安全性和可靠性的潜力。一个新的学习框架 SAL（分割与学习）通过从无标签数据中分离候选离群值然后使用这些候选离群值和标记的正态数据训练离群值分类器，理论上证明了 SAL 能以较小的错误率分离候选离群值，这为学习到的离群值分类器提供了泛化保证。实证结果表明 SAL 在常见基准测试上取得了最先进的性能。