On the Burden of Achieving Fairness in Conformal Prediction
Several recent research papers explore advancements in conformal prediction, a method for quantifying uncertainty in machine learning models. One paper introduces an efficient online conformal selection technique that requires less feedback, while another focuses on the trade-offs involved in achieving fairness in conformal prediction, highlighting tensions between coverage and set size. Additional research delves into new theoretical frameworks for conformal prediction, including methods that use transported beta laws, tighten coverage bounds through score transformation, and optimize prediction sets without data splitting by extending to multi-variable calibration. AI
IMPACT These papers advance theoretical understanding and practical application of uncertainty quantification in ML models.