bib-refs.bib

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%% ARTICLES %%%%%%%%%%%%%%%%%
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@article{Breiman-BaggingPredictors,
  author    = {Leo Breiman},
  title     = {Bagging Predictors},
  journal   = {Machine Learning},
  volume    = {24},
  number    = {2},
  pages     = {123--140},
  year      = {1996},
  url       = {https://doi.org/10.1007/BF00058655},
  doi       = {10.1007/BF00058655},
  timestamp = {Wed, 14 Nov 2018 10:27:03 +0100}
}

@article{Li-Hyperband,
  author    = {Lisha Li and
               Kevin G. Jamieson and
               Giulia DeSalvo and
               Afshin Rostamizadeh and
               Ameet Talwalkar},
  title     = {Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits},
  journal   = {CoRR},
  volume    = {abs/1603.06560},
  year      = {2016},
  url       = {http://arxiv.org/abs/1603.06560},
  archivePrefix = {arXiv},
  eprint    = {1603.06560},
  timestamp = {Mon, 13 Aug 2018 16:48:11 +0200},
}

@article{Jamieson-SuccessiveHalving,
  author    = {Kevin G. Jamieson and
               Ameet Talwalkar},
  title     = {Non-stochastic Best Arm Identification and Hyperparameter Optimization},
  journal   = {CoRR},
  volume    = {abs/1502.07943},
  year      = {2015},
  url       = {http://arxiv.org/abs/1502.07943},
  archivePrefix = {arXiv},
  eprint    = {1502.07943},
  timestamp = {Mon, 13 Aug 2018 16:46:53 +0200},
}

@article{Mohr-ML-Plan,
  author    = {Felix Mohr and Marcel Wever and Eyke H{\"{u}}llermeier},
  title     = {ML-Plan: Automated machine learning via hierarchical planning},
  journal   = {Machine Learning},
  volume    = {107},
  number    = {8-10},
  pages     = {1495--1515},
  year      = {2018},
  url       = {https://doi.org/10.1007/s10994-018-5735-z},
  doi       = {10.1007/s10994-018-5735-z},
  timestamp = {Wed, 01 Aug 2018 13:10:15 +0200}
}

@article{Frazier-Bayesian-Optimization,
  author    = {Peter I. Frazier},
  title     = {A Tutorial on Bayesian Optimization},
  journal   = {CoRR},
  volume    = {abs/1807.02811},
  year      = {2018},
  url       = {http://arxiv.org/abs/1807.02811},
  archivePrefix = {arXiv},
  eprint    = {1807.02811},
  timestamp = {Mon, 13 Aug 2018 16:48:03 +0200},
}

@article{Wolpert-No-Free-Lunch-Theorems,
  author    = {David H. Wolpert and
               William G. Macready},
  title     = {No free lunch theorems for optimization},
  journal   = {{IEEE} Trans. Evolutionary Computation},
  volume    = {1},
  number    = {1},
  pages     = {67--82},
  year      = {1997},
  url       = {https://doi.org/10.1109/4235.585893},
  doi       = {10.1109/4235.585893},
  timestamp = {Tue, 08 May 2018 14:46:40 +0200},
}

@article{Quemy-Two-Stage-Optimization,
  author    = {Alexandre Quemy},
  title     = {Two-stage Optimization for Machine Learning Workflow},
  journal   = {CoRR},
  volume    = {abs/1907.00678},
  year      = {2019},
  url       = {http://arxiv.org/abs/1907.00678},
  archivePrefix = {arXiv},
  eprint    = {1907.00678},
  timestamp = {Mon, 08 Jul 2019 14:12:33 +0200},
}

@article{Thompson-Sampling,
  author =       "William R. Thompson",
  title =        "On the Likelihood that One Unknown Probability Exceeds
                 Another in View of the Evidence of Two Samples",
  journal =      j-BIOMETRIKA,
  volume =       "25",
  number =       "3/4",
  pages =        "285--294",
  month =        dec,
  year =         "1933",
  DOI =          "https://doi.org/10.2307/2332286",
}

@article{Auer-UCB1,
  author    = {Peter Auer and
               Nicol{\`{o}} Cesa{-}Bianchi and
               Paul Fischer},
  title     = {Finite-time Analysis of the Multiarmed Bandit Problem},
  journal   = {Mach. Learn.},
  volume    = {47},
  number    = {2-3},
  pages     = {235--256},
  year      = {2002},
  url       = {https://doi.org/10.1023/A:1013689704352},
  doi       = {10.1023/A:1013689704352},
}

@article{Goldberg-Boltzmann,
  author    = {David E. Goldberg},
  title     = {A Note on Boltzmann Tournament Selection for Genetic Algorithms and
               Population-Oriented Simulated Annealing},
  journal   = {Complex Systems},
  volume    = {4},
  number    = {4},
  year      = {1990},
  url       = {http://www.complex-systems.com/abstracts/v04\_i04\_a05.html},
}

@article{Pedregosa-Scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
          and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
          and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
          Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}

@article{Virtanen-SciPy,
       author = {{Virtanen}, Pauli and {Gommers}, Ralf and {Oliphant},
         Travis E. and {Haberland}, Matt and {Reddy}, Tyler and
         {Cournapeau}, David and {Burovski}, Evgeni and {Peterson}, Pearu
         and {Weckesser}, Warren and {Bright}, Jonathan and {van der Walt},
         St{\'e}fan J.  and {Brett}, Matthew and {Wilson}, Joshua and
         {Jarrod Millman}, K.  and {Mayorov}, Nikolay and {Nelson}, Andrew
         R.~J. and {Jones}, Eric and {Kern}, Robert and {Larson}, Eric and
         {Carey}, CJ and {Polat}, {\.I}lhan and {Feng}, Yu and {Moore},
         Eric W. and {Vand erPlas}, Jake and {Laxalde}, Denis and
         {Perktold}, Josef and {Cimrman}, Robert and {Henriksen}, Ian and
         {Quintero}, E.~A. and {Harris}, Charles R and {Archibald}, Anne M.
         and {Ribeiro}, Ant{\^o}nio H. and {Pedregosa}, Fabian and
         {van Mulbregt}, Paul and {Contributors}, SciPy 1. 0},
        title = "{SciPy 1.0: Fundamental Algorithms for Scientific
                  Computing in Python}",
      journal = {Nature Methods},
      year = "2020",
      volume={17},
      pages={261--272},
      adsurl = {https://rdcu.be/b08Wh},
      doi = {https://doi.org/10.1038/s41592-019-0686-2},
}

@article{Vanschoren-OpenML,
  author    = {Joaquin Vanschoren and
               Jan N. van Rijn and
               Bernd Bischl and
               Luìs Torgo},
  title     = {OpenML: networked science in machine learning},
  journal   = {{SIGKDD} Explorations},
  volume    = {15},
  number    = {2},
  pages     = {49--60},
  year      = {2013},
  url       = {https://doi.org/10.1145/2641190.2641198},
  doi       = {10.1145/2641190.2641198},
}

@article{Kurtzer-Singularity,
    author = {Kurtzer, Gregory M. AND Sochat, Vanessa AND Bauer, Michael W.},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Singularity: Scientific containers for mobility of compute},
    year = {2017},
    month = {05},
    volume = {12},
    url = {https://doi.org/10.1371/journal.pone.0177459},
    pages = {1-20},
    abstract = {Here we present Singularity, software developed to bring containers and reproducibility to scientific computing. Using Singularity containers, developers can work in reproducible environments of their choosing and design, and these complete environments can easily be copied and executed on other platforms. Singularity is an open source initiative that harnesses the expertise of system and software engineers and researchers alike, and integrates seamlessly into common workflows for both of these groups. As its primary use case, Singularity brings mobility of computing to both users and HPC centers, providing a secure means to capture and distribute software and compute environments. This ability to create and deploy reproducible environments across these centers, a previously unmet need, makes Singularity a game changing development for computational science.},
    number = {5},
    doi = {10.1371/journal.pone.0177459}
}

@article{Browne-Policies,
  author={C. B. {Browne} and E. {Powley} and D. {Whitehouse} and S. M. {Lucas} and P. I. {Cowling} and P. {Rohlfshagen} and S. {Tavener} and D. {Perez} and S. {Samothrakis} and S. {Colton}},
  journal={IEEE Transactions on Computational Intelligence and AI in Games}, 
  title={A Survey of Monte Carlo Tree Search Methods}, 
  year={2012},
  volume={4},
  number={1},
  pages={1-43},
}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%% BOOKS %%%%%%%%%%%%%%%%%%
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@book{Goodfellow-DeepLearning,
    title={Deep Learning},
    author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
    publisher={MIT Press},
    note={\url{http://www.deeplearningbook.org}},
    year={2016}
}

@book{Mitchell-MachineLearning,
  author    = {Tom M. Mitchell},
  title     = {Machine learning},
  series    = {McGraw Hill series in computer science},
  publisher = {McGraw-Hill},
  year      = {1997},
  url       = {http://www.worldcat.org/oclc/61321007},
  isbn      = {978-0-07-042807-2},
  timestamp = {Wed, 26 Apr 2017 17:48:08 +0200}
}

@book{Boyd-Optimization,
  author    = {Stephen P. Boyd and
               Lieven Vandenberghe},
  title     = {Convex Optimization},
  publisher = {Cambridge University Press},
  year      = {2014},
  url       = {https://web.stanford.edu/\%7Eboyd/cvxbook/},
  doi       = {10.1017/CBO9780511804441},
  isbn      = {978-0-521-83378-3}
}

@book{Witten-Weka,
  author = {Ian H. Witten and Eibe Frank and Mark A. Hall and Christopher J. Pal},
  title = {Data Mining: Practical Machine Learning Tools and Techniques},
  publisher = {Morgan Kaufmann},
  year = 2016,
  address = {Burlington, MA},
  edition = 4,
  http = {http://www.cs.waikato.ac.nz/~ml/weka/book.html}
}

@book{Rasmussen-Gaussian-Processes,
  author    = {Carl Edward Rasmussen and
               Christopher K. I. Williams},
  title     = {Gaussian processes for machine learning},
  series    = {Adaptive computation and machine learning},
  publisher = {{MIT} Press},
  year      = {2006},
  url       = {http://www.worldcat.org/oclc/61285753},
  isbn      = {026218253X},
  timestamp = {Wed, 26 Apr 2017 17:48:08 +0200},
}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%% INCOLLECTION %%%%%%%%%%%%%%
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@incollection{Feurer-AutoSklearn,
   title = {Efficient and Robust Automated Machine Learning},
   author = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina and
             Springenberg, Jost and Blum, Manuel and Hutter, Frank},
   booktitle = {Advances in Neural Information Processing Systems 28},
   editor = {C. Cortes and N. D. Lawrence and D. D. Lee and M. Sugiyama and R. Garnett},
   pages = {2962--2970},
   year = {2015},
   publisher = {Curran Associates, Inc.},
   url = {http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf}
}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%% INPROCEEDINGS %%%%%%%%%%%%%%
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@inproceedings{Feurer-Cash,
  author    = {Matthias Feurer and
               Aaron Klein and
               Katharina Eggensperger and
               Jost Tobias Springenberg and
               Manuel Blum and
               Frank Hutter},
  editor    = {Corinna Cortes and
               Neil D. Lawrence and
               Daniel D. Lee and
               Masashi Sugiyama and
               Roman Garnett},
  title     = {Efficient and Robust Automated Machine Learning},
  booktitle = {Advances in Neural Information Processing Systems 28: Annual Conference
               on Neural Information Processing Systems 2015, December 7-12, 2015,
               Montreal, Quebec, Canada},
  pages     = {2962--2970},
  year      = {2015},
  url       = {http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning},
  timestamp = {Wed, 26 Oct 2016 14:24:10 +0200}
}

@inproceedings{Olson-Tpot,
  author    = {Randal S. Olson and
               Jason H. Moore},
  title     = {{TPOT:} {A} Tree-based Pipeline Optimization Tool for Automating Machine
               Learning},
  booktitle = {Proceedings of the 2016 Workshop on Automatic Machine Learning, AutoML
               2016, co-located with 33rd International Conference on Machine Learning
               {(ICML} 2016), New York City, NY, USA, June 24, 2016},
  pages     = {66--74},
  year      = {2016},
  url       = {http://proceedings.mlr.press/v64/olson\_tpot\_2016.html},
  timestamp = {Wed, 29 May 2019 08:41:45 +0200},
}

@inproceedings{Guimar-Recipe,
  author    = {Alex Guimar{\~{a}}es Cardoso de S{\'{a}} and
               Walter Jos{\'{e}} G. S. Pinto and
               Luiz Ot{\'{a}}vio Vilas Boas Oliveira and
               Gisele L. Pappa},
  title     = {{RECIPE:} {A} Grammar-Based Framework for Automatically Evolving Classification
               Pipelines},
  booktitle = {Genetic Programming - 20th European Conference, EuroGP 2017, Amsterdam,
               The Netherlands, April 19-21, 2017, Proceedings},
  pages     = {246--261},
  year      = {2017},
  url       = {https://doi.org/10.1007/978-3-319-55696-3\_16},
  doi       = {10.1007/978-3-319-55696-3\_16},
}

@inproceedings{Hutter-SMAC,
  author    = {Frank Hutter and
               Holger H. Hoos and
               Kevin Leyton{-}Brown},
  editor    = {Carlos A. Coello Coello},
  title     = {Sequential Model-Based Optimization for General Algorithm Configuration},
  booktitle = {Learning and Intelligent Optimization - 5th International Conference,
               {LION} 5, Rome, Italy, January 17-21, 2011. Selected Papers},
  series    = {Lecture Notes in Computer Science},
  volume    = {6683},
  pages     = {507--523},
  publisher = {Springer},
  year      = {2011},
  url       = {https://doi.org/10.1007/978-3-642-25566-3\_40},
  doi       = {10.1007/978-3-642-25566-3\_40},
}

@inproceedings{Thornton-AutoWeka,
  author    = {Chris Thornton and
               Frank Hutter and
               Holger H. Hoos and
               Kevin Leyton{-}Brown},
  title     = {Auto-WEKA: combined selection and hyperparameter optimization of classification
               algorithms},
  booktitle = {The 19th {ACM} {SIGKDD} International Conference on Knowledge Discovery
               and Data Mining, {KDD} 2013, Chicago, IL, USA, August 11-14, 2013},
  pages     = {847--855},
  year      = {2013},
  url       = {https://doi.org/10.1145/2487575.2487629},
  doi       = {10.1145/2487575.2487629},
  timestamp = {Wed, 14 Nov 2018 10:52:07 +0100},
}

@inproceedings{Sun-ReinBo,
  author    = {Xudong Sun and
               Jiali Lin and
               Bernd Bischl},
  editor    = {Peggy Cellier and
               Kurt Driessens},
  title     = {ReinBo: Machine Learning Pipeline Conditional Hierarchy Search and
               Configuration with Bayesian Optimization Embedded Reinforcement Learning},
  booktitle = {Machine Learning and Knowledge Discovery in Databases - International
               Workshops of {ECML} {PKDD} 2019, W{\"{u}}rzburg, Germany, September
               16-20, 2019, Proceedings, Part {I}},
  series    = {Communications in Computer and Information Science},
  volume    = {1167},
  pages     = {68--84},
  publisher = {Springer},
  year      = {2019},
  url       = {https://doi.org/10.1007/978-3-030-43823-4\_7},
  doi       = {10.1007/978-3-030-43823-4\_7},
  timestamp = {Tue, 31 Mar 2020 12:20:45 +0200},
}

@inproceedings{Kocsis-UCT,
  author    = {Levente Kocsis and
               Csaba Szepesv{\'{a}}ri},
  editor    = {Johannes F{\"{u}}rnkranz and
               Tobias Scheffer and
               Myra Spiliopoulou},
  title     = {Bandit Based Monte-Carlo Planning},
  booktitle = {Machine Learning: {ECML} 2006, 17th European Conference on Machine
               Learning, Berlin, Germany, September 18-22, 2006, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {4212},
  pages     = {282--293},
  publisher = {Springer},
  year      = {2006},
  url       = {https://doi.org/10.1007/11871842\_29},
  doi       = {10.1007/11871842\_29},
  timestamp = {Tue, 14 May 2019 10:00:54 +0200},
}

@inproceedings{Rakotoarison-Mosaic,
  title     = {Automated Machine Learning with Monte-Carlo Tree Search},
  author    = {Rakotoarison, Herilalaina and Schoenauer, Marc and Sebag, Michèle},
  booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on
               Artificial Intelligence, {IJCAI-19}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},             
  pages     = {3296--3303},
  year      = {2019},
  month     = {7},
  doi       = {10.24963/ijcai.2019/457},
  url       = {https://doi.org/10.24963/ijcai.2019/457},
}

@inproceedings{Kocsis-UCT,
  author    = {Levente Kocsis and
               Csaba Szepesv{\'{a}}ri},
  title     = {Bandit Based Monte-Carlo Planning},
  booktitle = {Machine Learning: {ECML} 2006, 17th European Conference on Machine
               Learning, Berlin, Germany, September 18-22, 2006, Proceedings},
  pages     = {282--293},
  year      = {2006},
  url       = {https://doi.org/10.1007/11871842\_29},
  doi       = {10.1007/11871842\_29},
  timestamp = {Tue, 14 May 2019 10:00:54 +0200},
}

@inproceedings{Lindauer-Smac-Warmstart,
  author    = {Marius Lindauer and
               Frank Hutter},
  title     = {Warmstarting of Model-Based Algorithm Configuration},
  booktitle = {Proceedings of the Thirty-Second {AAAI} Conference on Artificial Intelligence,
               (AAAI-18), the 30th innovative Applications of Artificial Intelligence
               (IAAI-18), and the 8th {AAAI} Symposium on Educational Advances in
               Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February
               2-7, 2018},
  pages     = {1355--1362},
  year      = {2018},
  url       = {https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17235},
}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%% TECHREPORT %%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@techreport{Platt-SMO,
author = {Platt, John},
title = {Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines},
year = {1998},
month = {April},
abstract = {This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization , or SMO . Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems. These small QP problems are solved analytically, which avoids using a time-consuming numerical QP optimization as an inner loop. The amount of memory required for SMO is linear in the training set size, which allows SMO to handle very large training sets. Because matrix computation is avoided, SMO scales somewhere between linear and quadratic in the training set size for various test problems, while the standard chunking SVM algorithm scales somewhere between linear and cubic in the training set size. SMO's computation time is dominated by SVM evaluation, hence SMO is fastest for linear SVMs and sparse data sets. On real- world sparse data sets, SMO can be more than 1000 times faster than the chunking algorithm.},
url = {https://www.microsoft.com/en-us/research/publication/sequential-minimal-optimization-a-fast-algorithm-for-training-support-vector-machines/},
pages = {21},
number = {MSR-TR-98-14},
}