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Found 6 papers, 2 papers with code
AMLB: an AutoML Benchmark
Comparing different AutoML frameworks is notoriously challenging and often done incorrectly.
Multi-Objective Hyperparameter Optimization -- An Overview
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows.
Automatic Componentwise Boosting: An Interpretable AutoML System
Despite its restriction to an interpretable model space, our system is competitive in terms of predictive performance on most data sets while being more user-friendly and transparent.
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
Most machine learning algorithms are configured by one or several hyperparameters that must be carefully chosen and often considerably impact performance.
Multi-Objective Automatic Machine Learning with AutoxgboostMC
AutoML systems are currently rising in popularity, as they can build powerful models without human oversight.
Automatic Gradient Boosting
Automatic machine learning performs predictive modeling with high performing machine learning tools without human interference.
