Conformal Predictive Distributions with Kernels.

This paper reviews the checkered history of predictive distributions in statistics and discusses two developments, one from recent literature and the other new. The first development is bringing predictive distributions into machine learning, whose early development was so deeply influenced by two remarkable groups at the Institute of Automation and Remote Control. As result, they become more robust and their validity ceases to depend on Bayesian or narrow parametric assumptions. The second development is combining predictive distributions with kernel methods, which were originated by one of those groups, including Emmanuel Braverman. As result, they become more flexible and, therefore, their predictive efficiency improves significantly for realistic non-linear data sets.