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For many machine learning models, a choice of hyperparameters is a crucial step towards achieving high performance.
Efficient global optimization of expensive black-box functions
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A comparison of ranking methods for classification algorithm selection
Brazdil, P. B. and Soares, C. (2000) · 2000
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Meta-learning by landmarking various learning algorithms
Pfahringer, B., Bensusan, H., and Giraud-Carrier, C. G. (2000) · 2000
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Random forests
Breiman, L. (2001) · 2001
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Towards explainable meta-learning
Woźnica, K. and Biecek, P. (2021) · 2002
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Using meta-learning to support data mining
Vilalta, R., Giraud-Carrier, C., Brazdil, P., and Soares, C. (2004) · 2004
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Quantifying the impact of learning algorithm parameter tuning
Lavesson, N. and Davidsson, P. (2006) · 2006
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Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., and Hutter, F. (2021) · 2007
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MementoML: Performance of selected machine learning algorithm configurations on OpenML100 datasets
Kretowicz, W. and Biecek, P. (2020) · 2008
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Algorithms for hyper-parameter optimization
Bergstra, J., Bardenet, R., Bengio, Y., and Kégl, B. (2011) · 2011
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Sequential model-based optimization for general algorithm configuration
Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2011) · 2011
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Random search for hyper-parameter optimization
Bergstra, J. and Bengio, Y. (2012) · 2012
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Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P. (2012) · 2012
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Auto-WEKA
Thornton, C., Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2013) · 2013
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Automatic classifier selection for non-experts
Reif, M., Shafait, F., Goldstein, M., Breuel, T., and Dengel, A. (2014) · 2014
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Hyperopt: a python library for model selection and hyperparameter optimization
Bergstra, J., Komer, B., Eliasmith, C., Yamins, D., and Cox, D. D. (2015) · 2015
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Initializing bayesian hyperparameter optimization via meta-learning
Feurer, M., Springenberg, J., and Hutter, F. (2015) · 2015
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Learning hyperparameter optimization initializations
Wistuba, M., Schilling, N., and Schmidt-Thieme, L. (2015) · 2015
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XGBoost: A Scalable Tree Boosting System
Chen, T. and Guestrin, C. (2016) · 2016
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Non-stochastic best arm identification and hyperparameter optimization
Jamieson, K. and Talwalkar, A. (2016) · 2016
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Sequential model-free hyperparameter tuning
Wistuba, M., Schilling, N., and Schmidt-Thieme, L. (2016) · 2016
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OpenML benchmarking suites and the OpenML100
Bischl, B., Casalicchio, G., Feurer, M., Hutter, F., Lang, M., Mantovani, R. G., van Rijn, J. N., and Vanschoren, J. (2017) · 2017
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Towards a neural statistician
Edwards, H. and Storkey, A. (2017) · 2017
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Scalable gaussian process-based transfer surrogates for hyperparameter optimization
Wistuba, M., Schilling, N., and Schmidt-Thieme, L. (2018) · 2018
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Auto-sklearn: Efficient and Robust Automated Machine Learning
Feurer, M., Klein, A., Eggensperger, K., Springenberg, J. T., Blum, M., and Hutter, F. (2019) · 2019
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All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously
Fisher, A., Rudin, C., and Dominici, F. (2019) · 2019
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TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning
Olson, R. S. and Moore, J. H. (2019) · 2019
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Learning search spaces for bayesian optimization: Another view of hyperparameter transfer learning
Perrone, V., Shen, H., Seeger, M. W., Archambeau, C., and Jenatton, R. (2019) · 2019
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Reproducibility in critical care: a mortality prediction case study
Johnson, A. E. W., Pollard, T. J., and Mark, R. G. (2017) · 2017
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Hyperband: Bandit-based configuration evaluation for hyperparameter optimization
Li, L., Jamieson, K. G., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A. (2017) · 2017
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AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
Alaa, A. and Schaar, M. (2018) · 2018
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Annotative experts for hyperparameter selection
Davis, C. and Giraud-Carrier, C. (2018) · 2018
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BOHB: Robust and efficient hyperparameter optimization at scale
Falkner, S., Klein, A., and Hutter, F. (2018) · 2018
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Analysis of the AutoML Challenge Series 2015–2018
Guyon, I., Sun-Hosoya, L., Boullé, M., Escalante, H. J., Escalera, S., Liu, Z., Jajetic, D., Ray, B., Saeed, M., Sebag, M., Statnikov, A., Tu, W.-W., and Viegas, E. (2019) · 2018
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Tunability: Importance of hyperparameters of machine learning algorithms
Probst, P., Boulesteix, A.-L., and Bischl, B. (2019) · 2019
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Characterizing classification datasets: a study of meta-features for meta-learning
Rivolli, A., Garcia, L. P., Soares, C., Vanschoren, J., and de Carvalho, A. C. (2019) · 2019
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Meta-Learning
Vanschoren, J. (2019) · 2019
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Zhang, Z., Ho, K. M., and Hong, Y. (2019) · 2019
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MIMIC-IV (version 1.0)
Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L., and Mark, R. (2020) · 2020
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Dataset2vec: learning dataset meta-features
Jomaa, H. S., Schmidt-Thieme, L., and Grabocka, J. (2021) · 2021
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Machine learning for modeling the progression of alzheimer disease dementia using clinical data: a systematic literature review
Kumar, S., Oh, I., Schindler, S., Lai, A. M., Payne, P. R., and Gupta, A. (2021) · 2021
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Effects of high-flow oxygen therapy on patients with hypoxemia after extubation and predictors of reintubation: a retrospective study based on the MIMIC-IV database
Liu, T., Zhao, Q., and Du, B. (2021) · 2021
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MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset
Meng, C., Trinh, L., Xu, N., and Liu, Y. (2021) · 2021
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Learning multiple defaults for machine learning algorithms
Pfisterer, F., van Rijn, J. N., Probst, P., Müller, A. C., and Bischl, B. (2021) · 2021
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Bayesian Optimization with a Prior for the Optimum
Souza, A., Nardi, L., Oliveira, L., Olukotun, K., Lindauer, M., and Hutter, F. (2021) · 2021
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LightAutoML: AutoML Solution for a Large Financial Services Ecosystem
Vakhrushev, A., Ryzhkov, A., Savchenko, M., Simakov, D., Damdinov, R., and Tuzhilin, A. (2021) · 2021
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