no code implementations • 19 Apr 2022 • Jarad Forristal, Joshua Griffin, Wenwen Zhou, Seyedalireza Yektamaram
ARC methods are a relatively new family of optimization strategies that utilize a cubic-regularization (CR) term in place of trust-regions and line-searches.
no code implementations • 29 Sep 2021 • Steven Gardner, Oleg Golovidov, Joshua Griffin, Patrick Koch, Rui Shi, Brett Wujek, Yan Xu
There has been a recent surge of interest in fairness measurement and bias mitigation in machine learning, given the identification of significant disparities in predictions from models in many domains.
no code implementations • 14 Aug 2019 • Steven Gardner, Oleg Golovidov, Joshua Griffin, Patrick Koch, Wayne Thompson, Brett Wujek, Yan Xu
In this work, we present a framework called Autotune that effectively handles multiple objectives and constraints that arise in machine learning problems.
no code implementations • 1 May 2019 • Taiping He, Tao Wang, Ralph Abbey, Joshua Griffin
The support vector machines (SVM) algorithm is a popular classification technique in data mining and machine learning.
no code implementations • 1 Jul 2018 • Jennifer B. Erway, Joshua Griffin, Roummel F. Marcia, Riadh Omheni
Machine learning (ML) problems are often posed as highly nonlinear and nonconvex unconstrained optimization problems.
no code implementations • 20 Apr 2018 • Patrick Koch, Oleg Golovidov, Steven Gardner, Brett Wujek, Joshua Griffin, Yan Xu
For hyperparameter tuning, machine learning algorithms are complex black-boxes.