Multiple Kernel Learning for Efficient Conformal Predictions

Vineeth N Balasubramanian Shayok Chakraborty Sethuraman Panchanathan
Abstract The Conformal Predictions framework is a recent development in machine learning to associate reliable measures of confidence with results in classification and regression. This framework is founded on the principles of algorithmic randomness (Kolmogorov complexity), transductive inference and hypothesis testing. While the formulation of the framework guarantees validity, the efficiency of the framework depends greatly on the choice of the classifier and appropriate kernel functions or parameters. While this framework has ...