Author

A. Ribeiro

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Preprint Sep 2026

Bilinear World Models: Learning Representations with Structured Dynamics for Efficient Control

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 model in which, rather than learning arbitrary latent dynamics, we restrict them to follow a bilinear parameterization. This structure enables e...

Antonio Pariente, Ignacio Boero, Nikolai Matni et al. · 0 citations

#machine learning Preprint Sep 2026

Learning Samples Importance: Parameterizing Dual Variables in Everywhere Learning

This work characterize the error in the recovered primal solution induced by restricting the dual variable to a parametric function class and show that it is controlled by how well this class approximates the optimal statistical multiplier.

Ignacio Boero, Jonathan Nixon, A. Ribeiro · 0 citations

Preprint Jul 2026

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints

This work proposes a novel alignment framework that addresses the gap between per-sample constraints and average loss by enforcing per-sample constraints while still minimizing an average loss, and develops an augmented Lagrangian approach tailored to this formulation.

Ignacio Hounie, Ignacio Boero, A. Ribeiro · 2 citations