Weak Supervision Signal

Weak supervision leverages readily available, imperfect data sources (e.g., noisy labels, heuristics, or pre-trained models) to train machine learning models, addressing the limitations of fully supervised learning which requires extensive, expensive manual annotation. Current research focuses on improving the reliability and effectiveness of these weak signals, often employing techniques like reliability estimation, data re-weighting, and latent graph inference to mitigate the impact of noise and improve model generalization. This approach is particularly valuable in domains with limited labeled data, enabling the development of accurate and robust models for diverse applications, including natural language processing, graph inference, and reinforcement learning.