Fair AutoML Through Multi-objective Optimization

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.

AutoML Fairness

Constrained Multi-Objective Optimization for Automated Machine Learning

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.

BIG-bench Machine Learning Distributed Computing

Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning

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.

BIG-bench Machine Learning