2018 | Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features | Mojmír Mutný, et al. | NeurIPS | PDF
2018 | High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups. | PMLR | PDF
2016 | Bayesian Optimization with Robust Bayesian Neural Networks | Jost Tobias Springenberg， et al. | NIPS | PDF
2016 | Scalable Hyperparameter Optimization with Products of Gaussian Process Experts | Nicolas Schilling, et al. | PKDD | PDF
2016 | Taking the Human Out of the Loop: A Review of Bayesian Optimization | Bobak Shahriari, et al. | IEEE | PDF
2016 | Towards Automatically-Tuned Neural Networks | Hector Mendoza, et al. | JMLR | PDF
2016 | Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization | Martin Wistuba, et al. | PKDD | PDF
2015 | Efficient and Robust Automated Machine Learning | PDF
2015 | Hyperparameter Optimization with Factorized Multilayer Perceptrons | Nicolas Schilling, et al. | PKDD | PDF
2015 | Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization | Martin Wistua, et al. | PDF
2015 | Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons | Nicolas Schilling, et al. | ICTAI | PDF
2015 | Learning Hyperparameter Optimization Initializations | Martin Wistuba, et al. | DSAA | PDF
2015 | Scalable Bayesian optimization using deep neural networks | Jasper Snoek, et al. | ACM | PDF
2015 | Sequential Model-free Hyperparameter Tuning | Martin Wistuba, et al. | ICDM | PDF
2013 | Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms | PDF
2013 | Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures | J. Bergstra | JMLR | PDF
2012 | Practical Bayesian Optimization of Machine Learning Algorithms | PDF
2011 | Sequential Model-Based Optimization for General Algorithm Configuration(extended version) | PDF
Evolutionary Algorithms
2018 | Autostacker: A Compositional Evolutionary Learning System | Boyuan Chen, et al. | arXiv | PDF
2017 | Large-Scale Evolution of Image Classifiers | Esteban Real, et al. | PMLR | PDF
2016 | Automating biomedical data science through tree-based pipeline optimization | Randal S. Olson, et al. | ECAL | PDF
2016 | Evaluation of a tree-based pipeline optimization tool for automating data science | Randal S. Olson, et al. | GECCO | PDF
Lipschitz Functions
2017 | Global Optimization of Lipschitz functions | C´edric Malherbe, Nicolas Vayatis | arXiv | PDF
Local Search
2009 | ParamILS: An Automatic Algorithm Configuration Framework | Frank Hutter, et al. | JAIR | PDF
Meta Learning
2008 | Cross-Disciplinary Perspectives on Meta-Learning for Algorithm Selection | PDF
2019 | SMARTML: A Meta Learning-Based Framework for Automated Selection and Hyperparameter Tuning for Machine Learning Algorithms | PDF
Particle Swarm Optimization
2017 | Particle Swarm Optimization for Hyper-parameter Selection in Deep Neural Networks | Pablo Ribalta Lorenzo, et al. | GECCO | PDF
2008 | Particle Swarm Optimization for Parameter Determination and Feature Selection of Support Vector Machines | Shih-Wei Lin, et al. | Expert Systems with Applications | PDF
Random Search
2016 | Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization | Lisha Li, et al. | arXiv | PDF
2012 | Random Search for Hyper-Parameter Optimization | James Bergstra, Yoshua Bengio | JMLR | PDF
2011 | Algorithms for Hyper-parameter Optimization | James Bergstra, et al. | NIPS | PDF
Transfer Learning
2016 | Efficient Transfer Learning Method for Automatic Hyperparameter Tuning | Dani Yogatama, Gideon Mann | JMLR | PDF
2016 | Flexible Transfer Learning Framework for Bayesian Optimisation | Tinu Theckel Joy, et al. | PAKDD | PDF
2016 | Hyperparameter Optimization Machines | Martin Wistuba, et al. | DSAA | PDF
2013 | Collaborative Hyperparameter Tuning | R´emi Bardenet, et al. | ICML | PDF
Miscellaneous
2018 | Accelerating Neural Architecture Search using Performance Prediction | Bowen Baker, et al. | ICLR | PDF
2017 | Automatic Frankensteining: Creating Complex Ensembles Autonomously | Martin Wistuba, et al. | SIAM | PDF
Tutorials
Bayesian Optimization
2010 | A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning | PDF