5 papers
Position: Adopt Constraints Over Fixed Penalties in Deep Learning
Juan Ramirez, Meraj Hashemizadeh, Simon Lacoste-Julien
Recent efforts to develop trustworthy AI systems have increased interest in learning problems with explicit requirements, or constraints. In deep learning, however, such problems a…
Dual Optimistic Ascent (PI Control) is the Augmented Lagrangian Method in Disguise
Juan Ramirez, Simon Lacoste-Julien
Constrained optimization is a powerful framework for enforcing requirements on neural networks. These constrained deep learning problems are typically solved using first-order meth…
Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis
Yasamin Jalalian, Juan Felipe Osorio Ramirez, Alexander Hsu +2
We introduce a novel kernel-based framework for learning differential equations and their solution maps that is efficient in data requirements, in terms of solution examples and am…
Cooper: A Library for Constrained Optimization in Deep Learning
Jose Gallego-Posada, Juan Ramirez, Meraj Hashemizadeh +1
Cooper is an open-source package for solving constrained optimization problems involving deep learning models. Cooper implements several Lagrangian-based first-order update schemes…
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…