Reliable Prediction

Reliable prediction focuses on developing methods that not only achieve high accuracy but also provide trustworthy uncertainty estimates, crucial for safety-critical applications. Current research emphasizes techniques like conformal prediction, ensemble methods (including mutual-transport ensembles and assembled projection heads), and the use of pre-trained models to assess sample difficulty and improve calibration. These advancements aim to enhance the reliability of predictions across diverse domains, from image classification and natural language processing to federated learning and clinical decision-making, ultimately increasing trust and facilitating responsible deployment of AI systems.