Conformal Regression

Conformal regression is a statistical framework for constructing prediction intervals with guaranteed coverage probabilities, even for complex models like deep neural networks, addressing the challenge of reliable uncertainty quantification in regression tasks. Current research focuses on adapting conformal methods to handle non-exchangeable data (e.g., time series), improving efficiency by optimizing prediction interval width, and integrating conformal prediction with other techniques like reinforcement learning and Bayesian optimization for enhanced decision-making under uncertainty. This robust approach has significant implications for various fields, enabling more reliable predictions in safety-critical applications and improving the trustworthiness of machine learning models across diverse domains.