4 papers · 1 filter
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…
Bilinear World Models: Learning Representations with Structured Dynamics for Efficient Control
Antonio Pariente, Ignacio Boero, Nikolai Matni +1
World models jointly learn latent representations and dynamics that predict how high-dimensional observations evolve under actions. In this work, we propose a JEPA-style world mode…
Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints
Ignacio Hounie, Ignacio Boero, Alejandro Ribeiro
Fine-tuning language models often requires enforcing constraints on individual inputs without compromising downstream performance. Existing constrained alignment methods impose con…
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…