Practical Automated Machine Learning for the AutoML Challenge 2018
暂无分享,去创建一个
[1] Rich Caruana,et al. Ensemble selection from libraries of models , 2004, ICML.
[2] Pierre Geurts,et al. Extremely randomized trees , 2006, Machine Learning.
[3] Rich Caruana,et al. Getting the Most Out of Ensemble Selection , 2006, Sixth International Conference on Data Mining (ICDM'06).
[4] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[5] Kevin Leyton-Brown,et al. Sequential Model-Based Optimization for General Algorithm Configuration , 2011, LION.
[6] Kevin Leyton-Brown,et al. Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms , 2012, KDD.
[7] Luís Torgo,et al. OpenML: networked science in machine learning , 2014, SKDD.
[8] Andreas Krause,et al. Submodular Function Maximization , 2014, Tractability.
[9] Ran Gilad-Bachrach,et al. DART: Dropouts meet Multiple Additive Regression Trees , 2015, AISTATS.
[10] Aaron Klein,et al. Efficient and Robust Automated Machine Learning , 2015, NIPS.
[11] Frank Hutter,et al. Initializing Bayesian Hyperparameter Optimization via Meta-Learning , 2015, AAAI.
[12] Sergio Escalera,et al. Design of the 2015 ChaLearn AutoML challenge , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[13] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[14] Ameet Talwalkar,et al. Non-stochastic Best Arm Identification and Hyperparameter Optimization , 2015, AISTATS.
[15] Ameet Talwalkar,et al. Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization , 2016, ICLR.
[16] Aaron Klein,et al. BOHB: Robust and Efficient Hyperparameter Optimization at Scale , 2018, ICML.
[17] Sergio Escalera,et al. Analysis of the AutoML Challenge Series 2015-2018 , 2019, Automated Machine Learning.