• Automatic Frankensteining: Creating Complex Ensembles Autonomously
• Sequential Model-Based Optimization for General Algorithm Configuration (extended version)
• Towards Automatically-Tuned Neural Networks
• Learning Feature Engineering for Classification
• A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
• ParamILS: An Automatic Algorithm Configuration Framework
• AutoCompete: A Framework for Machine Learning Competitions
• Automating biomedical data science through tree-based pipeline optimization
• Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
• Learning to Optimize
• Neural Architecture Search with Reinforcement Learning
• Large-Scale Evolution of Image Classifiers
• Global optimization of Lipschitz functions
• One button machine for automating feature engineering in relational databases
• Feature Engineering for Predictive Modeling using Reinforcement Learning
• Simple And Efficient Architecture Search for Convolutional Neural Networks
• High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups
• Autostacker: A Compositional Evolutionary Learning System
• A Tutorial on Bayesian Optimization
• Taking the Human out of Learning Applications: A Survey on Automated Machine Learning
• Benchmark and Survey of Automated Machine Learning Frameworks
• Automated Machine Learning: State-of-The-Art and Open Challenges
• AutoML: A Survey of the State-of-the-Art
• Towards modular and programmable architecture search
• Auptimizer - an Extensible, Open-Source Framework for Hyperparameter Tuning
• Practical Bayesian Optimization of Machine Learning Algorithms
• Efficient and Robust Automated Machine Learning
• Bayesian Optimization with Robust Bayesian Neural Networks
• Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features
• Google Vizier: A Service for Black-Box Optimization
• Efficient Transfer Learning Method for Automatic Hyperparameter Tuning
• Accelerating Neural Architecture Search using Performance Prediction
• ATM: A distributed, collaborative, scalable system for automated machine learning
• Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms
• Collaborative hyperparameter tuning
• Random Search for Hyper-Parameter Optimization
• Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures
• Deep Feature Synthesis: Towards Automating Data Science Endeavors
• SmartML: A Meta Learning-Based Framework for Automated Selection and Hyperparameter Tuning for Machine Learning Algorithms
• Feature Selection as a One-Player Game
• Automating Feature Engineering
• Evolving Neural Networks through Augmenting Topologies
• AMC: AutoML for Model Compression and Acceleration on Mobile Devices