First-order Optimization for Superquantile-based Supervised Learning
This addresses distribution shift in machine learning applications, offering a method to improve robustness, though it appears incremental as it builds on existing superquantile regression with a new optimization approach.
The paper tackles the problem of supervised learning when test data distribution differs from training data by proposing a first-order optimization algorithm for superquantile-based learning, showing promising numerical results for safer predictions.
Classical supervised learning via empirical risk (or negative log-likelihood) minimization hinges upon the assumption that the testing distribution coincides with the training distribution. This assumption can be challenged in modern applications of machine learning in which learning machines may operate at prediction time with testing data whose distribution departs from the one of the training data. We revisit the superquantile regression method by proposing a first-order optimization algorithm to minimize a superquantile-based learning objective. The proposed algorithm is based on smoothing the superquantile function by infimal convolution. Promising numerical results illustrate the interest of the approach towards safer supervised learning.