Weak supervision (WS) frameworks are a popular way to bypass hand-labeling large datasets for training data-hungry models. These approaches synthesize multiple noisy but cheaply-acquired estimates of labels into a set of high-quality pseudolabels for downstream training. However, the synthesis technique is specific to a particular kind of label, such as binary labels or sequences, and each new label type requires manually designing a new synthesis algorithm. Instead, we propose a universal technique that enables weak supervision over any label type while still offering desirable properties, including practical flexibility, computational efficiency, and theoretical guarantees. We apply this technique to important problems previously not tackled by WS frameworks including learning to rank, regression, and learning in hyperbolic space. Theoretically, our synthesis approach produces a consistent estimators for learning some challenging but important generalizations of the exponential family model. Experimentally, we validate our framework and show improvement over baselines in diverse settings including real-world learning-to-rank and regression problems along with learning on hyperbolic manifolds.

翻译：弱监督框架是一种常见方法，用于替代大规模数据集的人工标注，以训练数据密集型模型。这类方法将多个带有噪声但获取成本低廉的标签估计值合成为一组高质量伪标签，用于下游训练。然而，现有的合成技术仅适用于特定标签类型（如二元标签或序列标签），且每种新标签类型都需要人工设计新的合成算法。为此，我们提出一种通用技术，可支持任意标签类型的弱监督，同时保持实用性、计算效率与理论保证等理想特性。我们将该技术应用于弱监督框架尚未解决的重要问题，包括学习排序、回归以及双曲空间学习。理论上，我们的合成方法可为学习指数族模型的一些具有挑战性但重要的推广形式提供一致估计量。实验验证表明，该框架在多种场景（包括真实世界的排序学习与回归问题，以及双曲流形学习）中均优于基线方法。