Distribution Calibration

Distribution calibration in machine learning aims to improve the reliability and accuracy of model predictions by correcting mismatches between predicted probabilities and actual outcomes. Current research focuses on addressing calibration challenges in various contexts, including active learning, noisy data, few-shot learning, and out-of-distribution generalization, often employing techniques like ensemble methods, Bayesian approaches, and optimal transport. These advancements are crucial for enhancing the trustworthiness and robustness of machine learning models across diverse applications, particularly in safety-critical domains where reliable uncertainty quantification is paramount.