Performance Estimation

Performance estimation focuses on accurately predicting the performance of machine learning models, particularly in challenging scenarios like data distribution shifts or limited labeled data. Current research emphasizes developing methods that are source-free (requiring no training data), robust to covariate shift (changes in data characteristics), and applicable across diverse data modalities and model architectures, including those based on generative models, optimal transport, and ensemble learning. These advancements are crucial for reliable model deployment and monitoring in real-world applications, improving decision-making in fields ranging from materials science and telecommunications to medical image analysis and personalized mobile sensing.