6 papers · 1 filter
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…
Composition and Alignment of Diffusion Models using Constrained Learning
Shervin Khalafi, Ignacio Hounie, Dongsheng Ding +1
Diffusion models have become prevalent in generative modeling due to their ability to sample from complex distributions. To improve the quality of generated samples and their compl…
Alignment of large language models with constrained learning
Botong Zhang, Shuo Li, Ignacio Hounie +3
We study the problem of computing an optimal large language model (LLM) policy for the constrained alignment problem, where the goal is to maximize a primary reward objective while…
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…
Feasible Learning
Juan Ramirez, Ignacio Hounie, Juan Elenter +4
We introduce Feasible Learning (FL), a sample-centric learning paradigm where models are trained by solving a feasibility problem that bounds the loss for each training sample. In…
Loss Shaping Constraints for Long-Term Time Series Forecasting
Ignacio Hounie, Javier Porras-Valenzuela, Alejandro Ribeiro
Several applications in time series forecasting require predicting multiple steps ahead. Despite the vast amount of literature in the topic, both classical and recent deep learning…