7 papers
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…
AL-CoLe: Augmented Lagrangian for Constrained Learning
Ignacio Boero, Ignacio Hounie, Alejandro Ribeiro
Despite the non-convexity of most modern machine learning parameterizations, Lagrangian duality has become a popular tool for addressing constrained learning problems. We revisit A…
Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset
Ignacio Boero, Santiago Diaz, Tomás Vázquez +3
The Optimal Reactive Power Dispatch (ORPD) problem plays a crucial role in power system operations, ensuring voltage stability and minimizing power losses. Recent advances in machi…
Navigation with shadow prices to optimize multi-commodity flow rates
Ignacio Boero, Igor Spasojevic, Mariana del Castillo +3
We propose a method for providing communication network infrastructure in autonomous multi-agent teams. In particular, we consider a set of communication agents that are placed alo…