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Most machine learning algorithms are configured by one or several hyperparameters that must be carefully chosen and often considerably impact performance.
“mlr3: A modern object-oriented machine learning framework in R”
Michel Lang et al · 1903
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“mlr3: A modern object-oriented machine learning framework in R”
Michel Lang et al · 1903
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“mlr3: A modern object-oriented machine learning framework in R”
Michel Lang et al · 1903
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“mlr3: A modern object-oriented machine learning framework in R”
Michel Lang et al · 1903
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“Automated Machine Learning: State-of-The-Art and Open Challenges”, 2019
Radwa Elshawi, Mohamed Maher and Sherif Sakr · 1906
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“Automated Machine Learning: State-of-The-Art and Open Challenges”, 2019
Radwa Elshawi, Mohamed Maher and Sherif Sakr · 1906
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“Auto-Keras: An Efficient Neural Architecture Search System”
H. Jin, Q. Song and X. Hu · 1956
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“Auto-Keras: An Efficient Neural Architecture Search System”
H. Jin, Q. Song and X. Hu · 1956
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“Nearest neighbor pattern classification”
T. Cover and P. Hart · 1967
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“Nearest neighbor pattern classification”
T. Cover and P. Hart · 1967
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“Ridge regression: Biased estimation for nonorthogonal problems”
Arthur. Hoerl and Robert. Kennard · 1970
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“Ridge regression: Biased estimation for nonorthogonal problems”
Arthur. Hoerl and Robert. Kennard · 1970
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“Generalized linear models”
John Nelder and Robert Wedderburn · 1972
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“A Transformation for Simplifying the Interpretation of Coefficients of Binary Variables in Regression Analysis”
Robert. Sweeney and Edwin. Ulveling · 1972
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“Generalized linear models”
John Nelder and Robert Wedderburn · 1972
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“A Transformation for Simplifying the Interpretation of Coefficients of Binary Variables in Regression Analysis”
Robert. Sweeney and Edwin. Ulveling · 1972
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“The Algorithm Selection Problem”
John. Rice · 1976
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“The Algorithm Selection Problem”
John. Rice · 1976
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“An economic method of computing LP τ \tau -sequences”
Ilya Antonov and VM Saleev · 1979
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“Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code”
MD McKay, RJ Beckman and WJ Conover · 1979
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“An economic method of computing LP τ \tau -sequences”
Ilya Antonov and VM Saleev · 1979
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“Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code”
MD McKay, RJ Beckman and WJ Conover · 1979
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“Classification and regression trees”
Leo Breiman, Jerome Friedman, Charles Stone and Richard Olshen · 1984
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“Classification and regression trees”
Leo Breiman, Jerome Friedman, Charles Stone and Richard Olshen · 1984
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“Improvements in multiprocessor system design”
David. Rodgers · 1985
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“Improvements in multiprocessor system design”
David. Rodgers · 1985
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“The strength of weak learnability”
Robert. Schapire · 1990
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“The strength of weak learnability”
Robert. Schapire · 1990
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“Stacked generalization”
David Wolpert · 1992
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“A training algorithm for optimal margin classifiers”
Bernhard. Boser, Isabelle. Guyon and Vladimir. Vapnik · 1992
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“Stacked generalization”
David Wolpert · 1992
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“A training algorithm for optimal margin classifiers”
Bernhard. Boser, Isabelle. Guyon and Vladimir. Vapnik · 1992
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“Hoeffding races: Accelerating model selection search for classification and function approximation”
Oded Maron and Andrew Moore · 1994
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“Hoeffding races: Accelerating model selection search for classification and function approximation”
Oded Maron and Andrew Moore · 1994
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“A study of cross-validation and bootstrap for accuracy estimation and model selection”
R. Kohavi · 1995
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“Oversearching and layered search in empirical learning”
J Quinlan and R Cameron-Jones · 1995
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“Support-vector networks”
Corinna Cortes and Vladimir Vapnik · 1995
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“Boosting a weak learning algorithm by majority”
Yoav Freund · 1995
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“A study of cross-validation and bootstrap for accuracy estimation and model selection”
R. Kohavi · 1995
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“Oversearching and layered search in empirical learning”
J Quinlan and R Cameron-Jones · 1995
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“Support-vector networks”
Corinna Cortes and Vladimir Vapnik · 1995
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“Boosting a weak learning algorithm by majority”
Yoav Freund · 1995
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“Support Vector Regression Machines”
Harris Drucker et al · 1996
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“Regression shrinkage and selection via the lasso”
Robert Tibshirani · 1996
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“Support Vector Regression Machines”
Harris Drucker et al · 1996
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“Regression shrinkage and selection via the lasso”
Robert Tibshirani · 1996
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“Preventing “overfitting” of cross-validation data”
Andrew Ng · 1997
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“A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting”
Yoav Freund and Robert Schapire · 1997
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“Preventing “overfitting” of cross-validation data”
Andrew Ng · 1997
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“A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting”
Yoav Freund and Robert Schapire · 1997
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“Efficient Global Optimization of Expensive Black-Box Functions”
Donald. Jones, Matthias Schonlau and William. Welch · 1998
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“Genetic optimization using derivatives”
Jasjeet Sekhon and Walter Mebane · 1998
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“An optimal algorithm for approximate nearest neighbor searching fixed dimensions”
Sunil Arya et al · 1998
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“Efficient Global Optimization of Expensive Black-Box Functions”
Donald. Jones, Matthias Schonlau and William. Welch · 1998
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“Genetic optimization using derivatives”
Jasjeet Sekhon and Walter Mebane · 1998
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“An optimal algorithm for approximate nearest neighbor searching fixed dimensions”
Sunil Arya et al · 1998
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“Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods”
John Platt · 1999
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“Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods”
John Platt · 1999
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“Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)”
Jerome Friedman, Trevor Hastie and Robert Tibshirani · 2000
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“New support vector algorithms”
Bernhard Schölkopf, Alex. Smola, Robert. Williamson and Peter. Bartlett · 2000
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“Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)”
Jerome Friedman, Trevor Hastie and Robert Tibshirani · 2000
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“New support vector algorithms”
Bernhard Schölkopf, Alex. Smola, Robert. Williamson and Peter. Bartlett · 2000
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“A Taxonomy of Global Optimization Methods Based on Response Surfaces”
Donald. Jones · 2001
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“Estimation of distribution algorithms: A new tool for evolutionary computation”
Pedro Larrañaga and Jose Lozano · 2001
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“Random forests”
Leo Breiman · 2001
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“Greedy function approximation: a gradient boosting machine”
Jerome Friedman · 2001
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“A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems”
Daniele Micci-Barreca · 2001
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“A Taxonomy of Global Optimization Methods Based on Response Surfaces”
Donald. Jones · 2001
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“Estimation of distribution algorithms: A new tool for evolutionary computation”
Pedro Larrañaga and Jose Lozano · 2001
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“Random forests”
Leo Breiman · 2001
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“Greedy function approximation: a gradient boosting machine”
Jerome Friedman · 2001
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“A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems”
Daniele Micci-Barreca · 2001
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“Evolution strategies - A comprehensive introduction”
Hans-Georg Beyer and Hans-Paul Schwefel · 2002
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“A Racing Algorithm for Configuring Metaheuristics”
Mauro Birattari, Thomas Stützle, Luis Paquete and Klaus Varrentrapp · 2002
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“Exploration of metamodeling sampling criteria for constrained global optimization”
Michael Sasena, Panos Papalambros and Pierre Goovaerts · 2002
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“SMOTE: Synthetic Minority Over-sampling Technique”
N.. Chawla, K.. Bowyer, L.. Hall and W.. Kegelmeyer · 2002
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“On the robustness of a simple domain reduction scheme for simulation‐based optimization”
N. Stander and K. Craig · 2002
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“Evolution strategies - A comprehensive introduction”
Hans-Georg Beyer and Hans-Paul Schwefel · 2002
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“A Racing Algorithm for Configuring Metaheuristics”
Mauro Birattari, Thomas Stützle, Luis Paquete and Klaus Varrentrapp · 2002
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“Exploration of metamodeling sampling criteria for constrained global optimization”
Michael Sasena, Panos Papalambros and Pierre Goovaerts · 2002
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“SMOTE: Synthetic Minority Over-sampling Technique”
N.. Chawla, K.. Bowyer, L.. Hall and W.. Kegelmeyer · 2002
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“On the robustness of a simple domain reduction scheme for simulation‐based optimization”
N. Stander and K. Craig · 2002
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“Model-based Asynchronous Hyperparameter Optimization”
Louis Tiao, Aaron Klein, Cedric Archambeau and Matthias Seeger · 2003
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“Hyper-Parameter Optimization: A Review of Algorithms and Applications”, 2020
Tong Yu and Hong Zhu · 2003
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“An Introduction to Variable and Feature Selection”
Isabelle Guyon and André Elisseeff · 2003
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“Model-based Asynchronous Hyperparameter Optimization”
Louis Tiao, Aaron Klein, Cedric Archambeau and Matthias Seeger · 2003
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“Hyper-Parameter Optimization: A Review of Algorithms and Applications”, 2020
Tong Yu and Hong Zhu · 2003
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“An Introduction to Variable and Feature Selection”
Isabelle Guyon and André Elisseeff · 2003
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“No unbiased estimator of the variance of k-fold cross-validation”
Yoshua Bengio and Yves Grandvalet · 2004
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“Weighted k-nearest-neighbor techniques and ordinal classification”, 2004
Klaus Hechenbichler and Klaus Schliep · 2004
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“kernlab – An S4 Package for Kernel Methods in R”
Alexandros Karatzoglou, Alex Smola, Kurt Hornik and Achim Zeileis · 2004
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“No unbiased estimator of the variance of k-fold cross-validation”
Yoshua Bengio and Yves Grandvalet · 2004
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“Weighted k-nearest-neighbor techniques and ordinal classification”, 2004
Klaus Hechenbichler and Klaus Schliep · 2004
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“kernlab – An S4 Package for Kernel Methods in R”
Alexandros Karatzoglou, Alex Smola, Kurt Hornik and Achim Zeileis · 2004
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“Regularization and variable selection via the elastic net”
Hui Zou and Trevor Hastie · 2005
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“Regularization and variable selection via the elastic net”
Hui Zou and Trevor Hastie · 2005
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“Evolution Strategies for Robust Optimization”
H.-G. Beyer and B. Sendhoff · 2006
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“Performance prediction and automated tuning of randomized and parametric algorithms”
Frank Hutter, Youssef Hamadi, Holger Hoos and Kevin Leyton-Brown · 2006
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“Gaussian processes for machine learning”
Carl Rasmussen and Christopher.. Williams · 2006
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“Thresholding for making classifiers cost-sensitive”
Victor Sheng and Charles Ling · 2006
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“Unbiased recursive partitioning: A conditional inference framework”
Torsten Hothorn, Kurt Hornik and Achim Zeileis · 2006
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“Evolution Strategies for Robust Optimization”
H.-G. Beyer and B. Sendhoff · 2006
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“Performance prediction and automated tuning of randomized and parametric algorithms”
Frank Hutter, Youssef Hamadi, Holger Hoos and Kevin Leyton-Brown · 2006
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“Gaussian processes for machine learning”
Carl Rasmussen and Christopher.. Williams · 2006
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“Thresholding for making classifiers cost-sensitive”
Victor Sheng and Charles Ling · 2006
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“Unbiased recursive partitioning: A conditional inference framework”
Torsten Hothorn, Kurt Hornik and Achim Zeileis · 2006
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“Evolutionary algorithms for solving multi-objective problems”
Carlos. Coello, Gary. Lamont and David. Van · 2007
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“Resampling Strategies for Model Assessment and Selection”
Richard Simon · 2007
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“Super Learner”
Mark. van Laan, Eric Polley and Alan. Hubbard · 2007
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“Does imputation matter? Benchmark for predictive models”, 2020
Katarzyna Woźnica and Przemysław Biecek · 2007
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“Evolutionary algorithms for solving multi-objective problems”
Carlos. Coello, Gary. Lamont and David. Van · 2007
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“Resampling Strategies for Model Assessment and Selection”
Richard Simon · 2007
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“Super Learner”
Mark. van Laan, Eric Polley and Alan. Hubbard · 2007
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“Does imputation matter? Benchmark for predictive models”, 2020
Katarzyna Woźnica and Przemysław Biecek · 2007
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“Evaluating microarray-based classifiers: an overview”
A-L Boulesteix, Carolin Strobl, Thomas Augustin and Martin Daumer · 2008
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“Standard errors for bagged and random forest estimators”
Joseph Sexton and Petter Laake · 2008
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“SATzilla: portfolio-based algorithm selection for SAT”
Lin Xu, Frank Hutter, Holger Hoos and Kevin Leyton-Brown · 2008
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“Building Predictive Models in R Using the caret Package”
Max Kuhn · 2008
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“Evaluating microarray-based classifiers: an overview”
A-L Boulesteix, Carolin Strobl, Thomas Augustin and Martin Daumer · 2008
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“Standard errors for bagged and random forest estimators”
Joseph Sexton and Petter Laake · 2008
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“SATzilla: portfolio-based algorithm selection for SAT”
Lin Xu, Frank Hutter, Holger Hoos and Kevin Leyton-Brown · 2008
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“Building Predictive Models in R Using the caret Package”
Max Kuhn · 2008
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“Particle swarm model selection.”
Hugo Escalante, Manuel Montes and Luis Sucar · 2009
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“The elements of statistical learning: data mining, inference, and prediction”
Trevor Hastie, Robert Tibshirani and Jerome Friedman · 2009
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“ParamILS: An Automatic Algorithm Configuration Framework”
Frank Hutter, Holger. Hoos, Kevin Leyton-Brown and Thomas Stützle · 2009
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“Direct Global Optimization Algorithm.”
Donald Jones · 2009
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“Particle swarm model selection.”
Hugo Escalante, Manuel Montes and Luis Sucar · 2009
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“The elements of statistical learning: data mining, inference, and prediction”
Trevor Hastie, Robert Tibshirani and Jerome Friedman · 2009
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“ParamILS: An Automatic Algorithm Configuration Framework”
Frank Hutter, Holger. Hoos, Kevin Leyton-Brown and Thomas Stützle · 2009
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“Direct Global Optimization Algorithm.”
Donald Jones · 2009
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“F-Race and iterated F-Race: An overview”
Mauro Birattari, Zhi Yuan, Prasanna Balaprakash and Thomas Stützle · 2010
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“Kriging Is Well-Suited to Parallelize Optimization”
David Ginsbourger, Rodolphe Le and Laurent Carraro · 2010
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“Model selection: Beyond the Bayesian/frequentist divide”
I. Guyon, A. Saffari, G. Dror and G. Cawley · 2010
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“Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design”
Niranjan Srinivas, Andreas Krause, Sham Kakade and Matthias Seeger · 2010
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“An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data”
Yufeng Ding and Jeffrey. Simonoff · 2010
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“Regularization Paths for Generalized Linear Models via Coordinate Descent”
Jerome Friedman, Trevor Hastie and Robert Tibshirani · 2010
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“Model-based Boosting 2.0”
Torsten Hothorn et al · 2010
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“F-Race and iterated F-Race: An overview”
Mauro Birattari, Zhi Yuan, Prasanna Balaprakash and Thomas Stützle · 2010
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“Kriging Is Well-Suited to Parallelize Optimization”
David Ginsbourger, Rodolphe Le and Laurent Carraro · 2010
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“Model selection: Beyond the Bayesian/frequentist divide”
I. Guyon, A. Saffari, G. Dror and G. Cawley · 2010
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“Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design”
Niranjan Srinivas, Andreas Krause, Sham Kakade and Matthias Seeger · 2010
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“An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data”
Yufeng Ding and Jeffrey. Simonoff · 2010
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“Regularization Paths for Generalized Linear Models via Coordinate Descent”
Jerome Friedman, Trevor Hastie and Robert Tibshirani · 2010
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“Model-based Boosting 2.0”
Torsten Hothorn et al · 2010
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“Optimally splitting cases for training and testing high dimensional classifiers”
Kevin Dobbin and Richard Simon · 2011
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“Sequential Model-Based Optimization for General Algorithm Configuration”
F. Hutter, H. Hoos and K. Leyton-Brown · 2011
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“Sequential model-based optimization for general algorithm configuration”
Frank Hutter, Holger Hoos and Kevin Leyton-Brown · 2011
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“Evaluating Learning Algorithms: A Classification Perspective”
Nathalie Japkowicz and Mohak Shah · 2011
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“Scikit-learn: Machine Learning in Python”
F. Pedregosa et al · 2011
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“The correlated knowledge gradient for simulation optimization of continuous parameters using gaussian process regression”
Warren Scott, Peter Frazier and Warren Powell · 2011
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“Algorithms for Hyper-Parameter Optimization”
J. Bergstra, R. Bardenet, Y. Bengio and B. Kégl · 2011
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“LIBSVM: A library for support vector machines”
Chih-Chung Chang and Chih-Jen Lin · 2011
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“Sequential Model-Based Optimization for General Algorithm Configuration”
F. Hutter, H. Hoos and K. Leyton-Brown · 2011
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“Genetic Optimization Using Derivatives: The rgenoud Package for R”
Walter. Mebane, Jr. and Jasjeet. Sekhon · 2011
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“DEoptim: An R Package for Global Optimization by Differential Evolution”
Katharine Mullen et al · 2011
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“Scikit-learn: Machine Learning in Python”
F. Pedregosa et al · 2011
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“cmaes: Covariance Matrix Adapting Evolution Strategy” R package version 1.0-11, 2011
Heike Trautmann, Olaf Mersmann and David Arnu · 2011
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“Optimally splitting cases for training and testing high dimensional classifiers”
Kevin Dobbin and Richard Simon · 2011
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“Sequential Model-Based Optimization for General Algorithm Configuration”
F. Hutter, H. Hoos and K. Leyton-Brown · 2011
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“Sequential model-based optimization for general algorithm configuration”
Frank Hutter, Holger Hoos and Kevin Leyton-Brown · 2011
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“Evaluating Learning Algorithms: A Classification Perspective”
Nathalie Japkowicz and Mohak Shah · 2011
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“Scikit-learn: Machine Learning in Python”
F. Pedregosa et al · 2011
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“The correlated knowledge gradient for simulation optimization of continuous parameters using gaussian process regression”
Warren Scott, Peter Frazier and Warren Powell · 2011
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“Algorithms for Hyper-Parameter Optimization”
J. Bergstra, R. Bardenet, Y. Bengio and B. Kégl · 2011
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“LIBSVM: A library for support vector machines”
Chih-Chung Chang and Chih-Jen Lin · 2011
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“Sequential Model-Based Optimization for General Algorithm Configuration”
F. Hutter, H. Hoos and K. Leyton-Brown · 2011
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“Genetic Optimization Using Derivatives: The rgenoud Package for R”
Walter. Mebane, Jr. and Jasjeet. Sekhon · 2011
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“DEoptim: An R Package for Global Optimization by Differential Evolution”
Katharine Mullen et al · 2011
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“Scikit-learn: Machine Learning in Python”
F. Pedregosa et al · 2011
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“cmaes: Covariance Matrix Adapting Evolution Strategy” R package version 1.0-11, 2011
Heike Trautmann, Olaf Mersmann and David Arnu · 2011
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“Random Search for Hyper-Parameter Optimization”
J. Bergstra and Y. Bengio · 2012
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“Random Search for Hyper-Parameter Optimization”
James Bergstra and Yoshua Bengio · 2012
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“Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation”
B. Bischl, O. Mersmann, H. Trautmann and C. Weihs · 2012
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“Entropy Search for Information-Efficient Global Optimization”
Philipp Hennig and Christian. Schuler · 2012
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“Parallel Algorithm Configuration”
Frank Hutter, Holger. Hoos and Kevin Leyton-Brown · 2012
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“DiceKriging, DiceOptim: Two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization”
Olivier Roustant, David Ginsbourger and Yves Deville · 2012
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“Practical Bayesian Optimization of Machine Learning Algorithms”
J. Snoek, H. Larochelle and R. Adams · 2012
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“Practical Bayesian Optimization of Machine Learning Algorithms”
Jasper Snoek, Hugo Larochelle and Ryan Adams · 2012
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“Multi-task Bayesian optimization”
K. Swersky, J. Snoek and R. Adams · 2012
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“Multi-Task Bayesian Optimization”
Kevin Swersky, Jasper Snoek and Ryan Adams · 2012
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“DEAP: Evolutionary Algorithms Made Easy”
F. Fortin et al · 2012
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“DiceKriging, DiceOptim: Two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization”
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“Practical Bayesian Optimization of Machine Learning Algorithms”
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“Random Search for Hyper-Parameter Optimization”
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“Random Search for Hyper-Parameter Optimization”
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“Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation”
“Nevergrad - A gradient-free optimization platform”
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“compboost: Modular Framework for Component-Wise Boosting”
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“Feature Selection Methods: Case of Filter and Wrapper Approaches for Maximising Classification Accuracy.”
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“Learning Transferable Architectures for Scalable Image Recognition”, 2018, pp. 8697–8710
Barret Zoph, Vijay Vasudevan, Jonathon Shlens and Quoc. Le · 2018
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B. Bischl, O. Mersmann, H. Trautmann and C. Weihs · 2012
Cited alongside, same era.
“Entropy Search for Information-Efficient Global Optimization”
Philipp Hennig and Christian. Schuler · 2012
Cited alongside, same era.
“Parallel Algorithm Configuration”
Frank Hutter, Holger. Hoos and Kevin Leyton-Brown · 2012
Cited alongside, same era.
“DiceKriging, DiceOptim: Two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization”
Olivier Roustant, David Ginsbourger and Yves Deville · 2012
Cited alongside, same era.
“Practical Bayesian Optimization of Machine Learning Algorithms”
J. Snoek, H. Larochelle and R. Adams · 2012
Cited alongside, same era.
“Practical Bayesian Optimization of Machine Learning Algorithms”
Jasper Snoek, Hugo Larochelle and Ryan Adams · 2012
Cited alongside, same era.
“Multi-task Bayesian optimization”
K. Swersky, J. Snoek and R. Adams · 2012
Cited alongside, same era.
Michael Chui et al · 2018
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“Efficient benchmarking of algorithm configurators via model-based surrogates”
Katharina Eggensperger et al · 2018
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“BOHB: Robust and Efficient Hyperparameter Optimization at Scale”
S. Falkner, A. Klein and F. Hutter · 2018
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“Bilevel Programming for Hyperparameter Optimization and Meta-Learning”
L. Franceschi et al · 2018
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“Dynamic Control of Explore/Exploit Trade-Off in Bayesian Optimization”
Dipti Jasrasaria and Edward Pyzer-Knapp · 2018
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“Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization”
Lisha Li et al · 2018
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“Warmstarting of Model-Based Algorithm Configuration”
Marius Lindauer and Frank Hutter · 2018
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“ML-Plan: Automated machine learning via hierarchical planning”
F. Mohr, M. Wever and E. Hüllermeier · 2018
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“Hyperparameter importance across datasets”
Jan Van and Frank Hutter · 2018
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“Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search”
Arber Zela, Aaron Klein, Stefan Falkner and Frank Hutter · 2018
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“adagio: Discrete and Global Optimization Routines” R package version 0.7.1, 2018
Hans Borchers · 2018
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“Generalized Linear Models With Examples in R”, Springer Texts in Statistics
Peter Dunn and Gordon Smyth · 2018
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