Regression Uncertainty Estimation

Regression uncertainty estimation focuses on quantifying the reliability of predictions made by regression models, aiming to provide not only a point estimate but also a measure of confidence in that estimate. Current research emphasizes improving the accuracy of uncertainty quantification, particularly under real-world conditions like data distribution shifts, using methods such as Gaussian processes with deep kernels and meta-learning techniques to calibrate model outputs. This area is crucial for deploying regression models in safety-critical applications, such as medical imaging, where understanding model limitations is paramount for reliable decision-making. The development of robust uncertainty estimation methods is a significant challenge, with recent work highlighting the need for more reliable benchmarks and algorithms that are less susceptible to overconfidence.