6 citations · 8 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Adaptive Learning of Tensor Network Structures
Meraj Hashemizadeh, Michelle Liu, Jacob Miller +1
Tensor Networks (TN) offer a powerful framework to efficiently represent very high-dimensional objects. TN have recently shown their potential for machine learning applications and…