1 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
Position: Adopt Constraints Over Fixed Penalties in Deep Learning
Juan Ramirez, Meraj Hashemizadeh, Simon Lacoste-Julien
Deep learning models are increasingly subject to explicit requirements, or constraints, such as safety levels, fairness limits, and sparsity budgets. In practice, such requirements…
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…
On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization
Motahareh Sohrabi, Juan Ramirez, Tianyue H. Zhang +2
Constrained optimization offers a powerful framework to prescribe desired behaviors in neural network models. Typically, constrained problems are solved via their min-max Lagrangia…