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Found 6 papers, 0 papers with code
A Novel Fast Exact Subproblem Solver for Stochastic Quasi-Newton Cubic Regularized Optimization
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.
Fair AutoML Through Multi-objective Optimization
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.
Constrained Multi-Objective Optimization for Automated Machine Learning
In this work, we present a framework called Autotune that effectively handles multiple objectives and constraints that arise in machine learning problems.
High-Performance Support Vector Machines and Its Applications
The support vector machines (SVM) algorithm is a popular classification technique in data mining and machine learning.
Trust-Region Algorithms for Training Responses: Machine Learning Methods Using Indefinite Hessian Approximations
Machine learning (ML) problems are often posed as highly nonlinear and nonconvex unconstrained optimization problems.
Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning
For hyperparameter tuning, machine learning algorithms are complex black-boxes.
