scientific article; zbMATH DE number 7164723

zbMath1441.62075MaRDI QIDQ5214214

Jaume Coll-Font, Setareh Ariafar, Dana H. Brooks, Jennifer G. Dy

Publication date: 7 February 2020

Full work available at URL: http://jmlr.csail.mit.edu/papers/v20/18-227.html

Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.

zbMATH Keywords

Gaussian processesexpected improvementAlternating Direction Method of Multipliers (ADMM) algorithmBayesian optimization (BO)

Mathematics Subject Classification ID

Gaussian processes (60G15) Bayesian inference (62F15) Sensitivity, stability, parametric optimization (90C31) Missing data (62D10)

Related Items (1)

SMGO-\(\Delta\): balancing caution and reward in global optimization with black-box constraints

Uses Software

• MNIST
• TensorFlow
• Keras
• Spearmint
• PESC
• Hyperopt

Cites Work

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• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• On the linear convergence of the alternating direction method of multipliers
• Efficient global optimization of expensive black-box functions
• Fast Bayesian hyperparameter optimization on large datasets
• Global convergence of ADMM in nonconvex nonsmooth optimization
• A taxonomy of global optimization methods based on response surfaces
• Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems
• Large-Scale Machine Learning with Stochastic Gradient Descent
• D-ADMM: A Communication-Efficient Distributed Algorithm for Separable Optimization
• Global optimization

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