scientific article; zbMATH DE number 7625207

MaRDI QIDQ5054669

Frank Hutter, Marius Lindauer, René Sass, Carolin Benjamins, Difan Deng, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Tim Ruhkopf

Publication date: 29 November 2022

Full work available at URL: https://arxiv.org/abs/2109.09831

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

zbMATH Keywords

algorithm configurationBayesian optimizationautomated machine learninghyperparameter optimizationmulti-fidelity optimization

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (8)

SMAC3 ⋮ MPILS: an automatic tuner for MILP solvers ⋮ The drone-assisted pickup and delivery problem: an adaptive large neighborhood search metaheuristic ⋮ An adaptive large neighbourhood search metaheuristic for hourly learning activity planning in personalised learning ⋮ NAS-PINN: neural architecture search-guided physics-informed neural network for solving PDEs ⋮ AutonoML: Towards an Integrated Framework for Autonomous Machine Learning ⋮ Targeted configuration of an SMT solver ⋮ Adaptive cut selection in mixed-integer linear programming

Uses Software

• irace
• Spearmint
• PyTorch
• Auto-WEKA
• SMAC
• Dask
• auto-sklearn
• IOHprofiler
• scikit-optimize

Cites Work

• Unnamed Item
• Unnamed Item
• Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
• The configurable SAT solver challenge (CSSC)
• Efficient global optimization of expensive black-box functions
• ParamILS: An Automatic Algorithm Configuration Framework
• Random forests

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