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Marius Thomas Lindauer | Kevin Leyton-Brown | Holger H. Hoos | Frank Hutter | Katharina Eggensperger | F. Hutter | H. Hoos | Kevin Leyton-Brown | Katharina Eggensperger | M. Lindauer
[1] Kevin Leyton-Brown,et al. Algorithm runtime prediction: Methods & evaluation , 2012, Artif. Intell..
[2] Teresa Bernarda Ludermir,et al. Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-regressors , 2008, ICANN.
[3] Nando de Freitas,et al. A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning , 2010, ArXiv.
[4] Yuri Malitsky,et al. Deep Learning for Algorithm Portfolios , 2016, AAAI.
[5] Jasper Snoek,et al. Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.
[6] Marius Thomas Lindauer,et al. A Portfolio Solver for Answer Set Programming: Preliminary Report , 2011, LPNMR.
[7] M. Fox,et al. The 3rd International Planning Competition: Results and Analysis , 2003, J. Artif. Intell. Res..
[8] Thomas Stützle,et al. AClib: A Benchmark Library for Algorithm Configuration , 2014, LION.
[9] Kevin Leyton-Brown,et al. SATzilla: Portfolio-based Algorithm Selection for SAT , 2008, J. Artif. Intell. Res..
[10] Luca Pulina,et al. A multi-engine approach to answer-set programming* , 2013, Theory and Practice of Logic Programming.
[11] Thomas Stützle,et al. Stochastic Local Search: Foundations & Applications , 2004 .
[12] Ricardo Vilalta,et al. Metalearning - Applications to Data Mining , 2008, Cognitive Technologies.
[13] Yuri Malitsky,et al. ISAC - Instance-Specific Algorithm Configuration , 2010, ECAI.
[14] Marius Thomas Lindauer,et al. The Configurable SAT Solver Challenge (CSSC) , 2015, Artif. Intell..
[15] R. Koenker. Quantile Regression: Fundamentals of Quantile Regression , 2005 .
[16] Alfonso Gerevini,et al. Automatic Generation of Efficient Domain-Optimized Planners from Generic Parametrized Planners , 2013, SOCS.
[17] C. Spearman. The proof and measurement of association between two things. , 2015, International journal of epidemiology.
[18] Lars Kotthoff,et al. Algorithm Selection for Combinatorial Search Problems: A Survey , 2012, AI Mag..
[19] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[20] Lars Schmidt-Thieme,et al. Hyperparameter Optimization with Factorized Multilayer Perceptrons , 2015, ECML/PKDD.
[21] Leslie Pérez Cáceres,et al. The irace package: Iterated racing for automatic algorithm configuration , 2016 .
[22] Ashish Sabharwal,et al. An Empirical Study of Optimization for Maximizing Diffusion in Networks , 2010, CP.
[23] Sonja Kuhnt,et al. Design and analysis of computer experiments , 2010 .
[24] Kevin Leyton-Brown,et al. Improved Features for Runtime Prediction of Domain-Independent Planners , 2014, ICAPS.
[25] Kevin Leyton-Brown,et al. Sequential Model-Based Optimization for General Algorithm Configuration , 2011, LION.
[26] David D. Cox,et al. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures , 2013, ICML.
[27] Julien Cornebise,et al. Weight Uncertainty in Neural Network , 2015, ICML.
[28] Hilan Bensusan,et al. Estimating the Predictive Accuracy of a Classifier , 2001, ECML.
[29] Uwe Schöning,et al. Choosing Probability Distributions for Stochastic Local Search and the Role of Make versus Break , 2012, SAT.
[30] R. Geoff Dromey,et al. An algorithm for the selection problem , 1986, Softw. Pract. Exp..
[31] Barry O'Sullivan,et al. Learning Sequential and Parallel Runtime Distributions for Randomized Algorithms , 2016, 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI).
[32] Pavel Brazdil,et al. Active Testing Strategy to Predict the Best Classification Algorithm via Sampling and Metalearning , 2010, ECAI.
[33] Marius Lindauer,et al. SpyBug: Automated Bug Detection in the Configuration Space of SAT Solvers , 2016, SAT.
[34] Yuliya Lierler,et al. Parsing Combinatory Categorial Grammar via Planning in Answer Set Programming , 2012, Correct Reasoning.
[35] Aaron Klein,et al. Bayesian Optimization with Robust Bayesian Neural Networks , 2016, NIPS.
[36] Ivan Serina,et al. LPG: A Planner Based on Local Search for Planning Graphs with Action Costs , 2002, AIPS.
[37] G. J. Hahn,et al. A Simple Method for Regression Analysis With Censored Data , 1979 .
[38] Yuri Malitsky,et al. Model-Based Genetic Algorithms for Algorithm Configuration , 2015, IJCAI.
[39] Shoaib Kamil,et al. OpenTuner: An extensible framework for program autotuning , 2014, 2014 23rd International Conference on Parallel Architecture and Compilation (PACT).
[40] Daniel S. Weld,et al. Temporal Planning with Continuous Change , 1994, AAAI.
[41] Armin Biere,et al. Automated Testing and Debugging of SAT and QBF Solvers , 2010, SAT.
[42] Piet Demeester,et al. A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design , 2010, J. Mach. Learn. Res..
[43] Aaron Klein,et al. Efficient and Robust Automated Machine Learning , 2015, NIPS.
[44] Michèle Sebag,et al. Collaborative hyperparameter tuning , 2013, ICML.
[45] Yoav Shoham,et al. Understanding Random SAT: Beyond the Clauses-to-Variables Ratio , 2004, CP.
[46] Samy Bengio,et al. A Parallel Mixture of SVMs for Very Large Scale Problems , 2001, Neural Computation.
[47] Thomas Stützle,et al. A Racing Algorithm for Configuring Metaheuristics , 2002, GECCO.
[48] Marius Thomas Lindauer,et al. claspfolio 2: Advances in Algorithm Selection for Answer Set Programming , 2014, Theory and Practice of Logic Programming.
[49] Alan J. Hu,et al. Boosting Verification by Automatic Tuning of Decision Procedures , 2007, Formal Methods in Computer Aided Design (FMCAD'07).
[50] Frank Hutter,et al. Initializing Bayesian Hyperparameter Optimization via Meta-Learning , 2015, AAAI.
[51] Kevin Leyton-Brown,et al. Bayesian Optimization With Censored Response Data , 2013, ArXiv.
[52] M. Lindauer,et al. Surviving Solver Sensitivity: An ASP Practitioner's Guide , 2012, ICLP.
[53] Armin Biere. Lingeling, Plingeling and Treengeling Entering the SAT Competition 2013 , 2013 .
[54] Kevin Leyton-Brown,et al. Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms , 2012, KDD.
[55] Sven Apel,et al. Cost-Efficient Sampling for Performance Prediction of Configurable Systems (T) , 2015, 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[56] Katharina Eggensperger,et al. Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters , 2013 .
[57] Nando de Freitas,et al. Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.
[58] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[59] Yuri Malitsky,et al. Algorithm Portfolios Based on Cost-Sensitive Hierarchical Clustering , 2013, IJCAI.
[60] João Gama,et al. Characterization of Classification Algorithms , 1995, EPIA.
[61] Kevin Leyton-Brown,et al. Automated Configuration of Mixed Integer Programming Solvers , 2010, CPAIOR.
[62] Alexander J. Smola,et al. Nonparametric Quantile Estimation , 2006, J. Mach. Learn. Res..
[63] Prabhat,et al. Scalable Bayesian Optimization Using Deep Neural Networks , 2015, ICML.
[64] Martin Gebser,et al. Conflict-driven answer set solving: From theory to practice , 2012, Artif. Intell..
[65] Armin Biere. Yet another Local Search Solver and Lingeling and Friends Entering the SAT Competition 2014 , 2014 .
[66] Andreas Dengel,et al. Automatic classifier selection for non-experts , 2012, Pattern Analysis and Applications.
[67] F. Hutter,et al. Hydra-MIP : Automated Algorithm Configuration and Selection for Mixed Integer Programming , 2011 .
[68] Nicolai Meinshausen,et al. Quantile Regression Forests , 2006, J. Mach. Learn. Res..
[69] Marius Thomas Lindauer,et al. Quantifying Homogeneity of Instance Sets for Algorithm Configuration , 2012, LION.
[70] Bernd Bischl,et al. Automatic model selection for high-dimensional survival analysis , 2015 .
[71] Kevin Leyton-Brown,et al. Efficient Benchmarking of Hyperparameter Optimizers via Surrogates , 2015, AAAI.
[72] Holger H. Hoos,et al. Analysing differences between algorithm configurations through ablation , 2015, Journal of Heuristics.
[73] Yoav Shoham,et al. Towards a universal test suite for combinatorial auction algorithms , 2000, EC '00.
[74] Carlos Ansótegui,et al. A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms , 2009, CP.
[75] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[76] Carlos Soares,et al. A Meta-Learning Method to Select the Kernel Width in Support Vector Regression , 2004, Machine Learning.
[77] Bernd Bischl,et al. ASlib: A benchmark library for algorithm selection , 2015, Artif. Intell..
[78] Marius Thomas Lindauer,et al. AutoFolio: An Automatically Configured Algorithm Selector , 2015, J. Artif. Intell. Res..
[79] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[80] Charles C. Taylor,et al. Meta-Analysis: From Data Characterisation for Meta-Learning to Meta-Regression , 2000 .
[81] Joaquin Vanschoren,et al. Selecting Classification Algorithms with Active Testing , 2012, MLDM.
[82] Marijn J. H. Heule,et al. Proceedings of SAT Competition 2013 , 2013 .
[83] Niklas Sörensson,et al. An Extensible SAT-solver , 2003, SAT.
[84] Jasper Snoek,et al. Bayesian Optimization with Unknown Constraints , 2014, UAI.
[85] Frank Hutter,et al. Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves , 2015, IJCAI.
[86] Lars Schmidt-Thieme,et al. Learning hyperparameter optimization initializations , 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).
[87] Yoav Shoham,et al. Empirical hardness models: Methodology and a case study on combinatorial auctions , 2009, JACM.
[88] Kevin Leyton-Brown,et al. Algorithm Runtime Prediction: Methods and Evaluation (Extended Abstract) , 2015, IJCAI.