Jonathan Nixon

Learning Samples Importance: Parameterizing Dual Variables in Everywhere Learning

Everywhere learning provides a principled framework for training AI models under constraints that must hold throughout the data distribution. In the dual domain, these pointwise constraints give rise to functional dual variables. In this work, we propose to learn these dual variables, motivated by the fact that their v...