Talk On Making Machine Learning Models Safer and Better: Data and Model Perspectives
B306 , EE DeptAbstract: As machine learning systems are increasingly deployed in real-world settings like healthcare, finance, and scientific applications, ensuring their safety and reliability is crucial. However, many state-of-the-art ML models still suffer from issues like poor out-of-distribution generalization, sensitivity to input corruptions, requiring large amounts of data, and inadequate calibration - limiting their robustness and trustworthiness […]