Accuracy Constraint

Accuracy constraint research focuses on improving the precision of models while managing limitations inherent in data or computational resources. Current efforts involve developing algorithms that learn constraints from limited or ambiguous data, such as positive-unlabeled learning for inferring continuous functions from demonstrations, and optimizing model architectures like Physics-Informed Neural Networks (PINNs) for enhanced accuracy. These advancements are crucial for various applications, including robotics, machine learning fairness, and efficient inference in large language models, by enabling more reliable and robust systems in scenarios with incomplete or noisy information.