2 citations · 6 across the 14 of their papers we have counts for
18 papers
Optimization and Optimizers for Adversarial Robustness
Hengyue Liang, Buyun Liang, Le Peng +3
Empirical robustness evaluation (RE) of deep learning models against adversarial perturbations entails solving nontrivial constrained optimization problems. Existing numerical algo…
NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning
Buyun Liang, Tim Mitchell, Ju Sun
Imposing explicit constraints is relatively new but increasingly pressing in deep learning, stimulated by, e.g., trustworthy AI that performs robust optimization over complicated p…
Optimization for Robustness Evaluation beyond Metrics
Hengyue Liang, Buyun Liang, Ying Cui +2
Empirical evaluation of deep learning models against adversarial attacks entails solving nontrivial constrained optimization problems. Popular algorithms for solving these constrai…
NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning
Buyun Liang, Tim Mitchell, Ju Sun
Optimizing nonconvex (NCVX) problems, especially nonsmooth and constrained ones, is an essential part of machine learning. However, it can be hard to reliably solve such problems w…
Root-max Problems, Hybrid Expansion-Contraction, and Quadratically Convergent Optimization of Passive Systems
Tim Mitchell, Paul Van Dooren
We present quadratically convergent algorithms to compute the extremal value of a real parameter for which a given rational transfer function of a linear time-invariant system is p…
On Properties of Univariate Max Functions at Local Maximizers
Tim Mitchell, Michael L. Overton
More than three decades ago, Boyd and Balakrishnan established a regularity result for the two-norm of a transfer function at maximizers. Their result extends easily to the stateme…