44 citations · 100 across the 12 of their papers we have counts for
16 papers
Optimization for One, Feasibility for Most: Decoupling Optimization from Feasibility in Chance-Constrained Programs
Justin Dumouchelle, Shunyu Yao, Beste Basciftci +1
Chance-constrained stochastic programs (CCSPs) require solutions that satisfy uncertain constraints with high probability. Standard approaches either embed scenarios directly into…
Optimal Combinatorial Testing with Constraints: The Balancing Act
Thiago Serra, Changkun Guan, Hunter Gehman +4
Imagine that you are in front of a cockpit with several on-off buttons. If you were to thoroughly test it, you would need to try a prohibitive number of configurations. But since m…
When Deep Learning Meets Polyhedral Theory: A Survey
Joey Huchette, Gonzalo Muñoz, Thiago Serra +1
In the past decade, deep learning became the prevalent methodology for predictive modeling thanks to the remarkable accuracy of deep neural networks in tasks such as computer visio…
Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions
Junyang Cai, Khai-Nguyen Nguyen, Nishant Shrestha +5
One surprising trait of neural networks is the extent to which their connections can be pruned with little to no effect on accuracy. But when we cross a critical level of parameter…
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm
Aidan Good, Jiaqi Lin, Hannah Sieg +5
Pruning techniques have been successfully used in neural networks to trade accuracy for sparsity. However, the impact of network pruning is not uniform: prior work has shown that t…
Optimal Decision Diagrams for Classification
Alexandre M. Florio, Pedro Martins, Maximilian Schiffer +2
Decision diagrams for classification have some notable advantages over decision trees, as their internal connections can be determined at training time and their width is not bound…