Consistent Learning

Consistent learning aims to develop machine learning models that reliably generalize across different data distributions, particularly in scenarios with limited labeled data or adversarial interactions. Current research focuses on improving consistency in semi-supervised learning through techniques like minimizing cross-sharpness between labeled and unlabeled data, and enhancing the robustness of learning algorithms by addressing issues like miscalibration and unbounded estimates. These advancements are crucial for improving the reliability and performance of machine learning models in various applications, including medical image analysis and game theory, where data scarcity or complex interactions pose significant challenges.