84 citations · 86 across the 4 of their papers we have counts for
4 papers
A Novel Fast Exact Subproblem Solver for Stochastic Quasi-Newton Cubic Regularized Optimization
Jarad Forristal, Joshua Griffin, Wenwen Zhou +1
In this work we describe an Adaptive Regularization using Cubics (ARC) method for large-scale nonconvex unconstrained optimization using Limited-memory Quasi-Newton (LQN) matrices.…
Constrained Multi-Objective Optimization for Automated Machine Learning
Steven Gardner, Oleg Golovidov, Joshua Griffin +4
Automated machine learning has gained a lot of attention recently. Building and selecting the right machine learning models is often a multi-objective optimization problem. General…
Trust-Region Algorithms for Training Responses: Machine Learning Methods Using Indefinite Hessian Approximations
Jennifer B. Erway, Joshua Griffin, Roummel F. Marcia +1
Machine learning (ML) problems are often posed as highly nonlinear and nonconvex unconstrained optimization problems. Methods for solving ML problems based on stochastic gradient d…
Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning
Patrick Koch, Oleg Golovidov, Steven Gardner +3
Machine learning applications often require hyperparameter tuning. The hyperparameters usually drive both the efficiency of the model training process and the resulting model quali…