Accuracy Guarantee

Accuracy guarantee in machine learning focuses on developing methods that provide quantifiable assurances about the reliability of model predictions, addressing the critical need for trustworthy AI systems. Current research emphasizes techniques like fitted likelihood estimation and deep kernel methods, alongside strategies for efficient model scaling and hyperparameter selection, often incorporating probabilistic generative models or neural network architectures. This work is significant because it moves beyond simply optimizing performance metrics towards providing verifiable confidence in model outputs, improving the reliability and applicability of machine learning across diverse domains.