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cs.LG2026
Learning Samples Importance: Parameterizing Dual Variables in Everywhere Learning
Ignacio Boero, Jonathan Nixon, Alejandro Ribeiro
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 co…
cs.LG2026
Everywhere Learning: Artificial Intelligence with Pointwise Constraints
Ignacio Boero, Ignacio Hounie, Luiz F. O. Chamon +2
Everywhere learning is a new paradigm whereby Artificial Intelligence (AI) systems are trained to satisfy loss constraints with probability one over the data distribution. This is…