Imperfect Prediction

Imperfect prediction research focuses on developing methods to quantify and mitigate the uncertainty inherent in machine learning predictions, particularly in high-stakes applications. Current efforts concentrate on refining techniques like conformal prediction, which provides statistically guaranteed prediction intervals, and adapting them to handle biases and limited prediction availability. This work is crucial for building trustworthy AI systems, enabling safer autonomous decision-making in diverse fields such as healthcare, robotics, and finance, by providing reliable uncertainty estimates and improving the robustness of predictions. The ultimate goal is to create more reliable and explainable AI systems that can be safely deployed in real-world scenarios.