4 papers
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 Chamon +1
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…