4 papers
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…
Balancing Act: Constraining Disparate Impact in Sparse Models
Meraj Hashemizadeh, Juan Ramirez, Rohan Sukumaran +3
Model pruning is a popular approach to enable the deployment of large deep learning models on edge devices with restricted computational or storage capacities. Although sparse mode…