Bayes Risk

Bayes risk quantifies the minimum expected error achievable by any classifier given a specific probability distribution of data and labels, serving as a benchmark for evaluating model performance. Current research focuses on understanding and minimizing Bayes risk in various contexts, including transfer learning, semi-supervised learning with uncertain labels, and multi-task learning, often employing Gaussian mixture models and Bayesian approaches for uncertainty quantification. This work has significant implications for improving the accuracy and reliability of machine learning models across diverse applications, from speech recognition to image classification, by providing theoretical frameworks for optimizing model design and evaluating performance against fundamental limits.