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Machine learning (ML) has become a vital part in many aspects of our daily life.
AlphaClean: Automatic Generation of Data Cleaning Pipelines
Krishnan, S., and Wu, E. (2019) · 1904
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Automated Machine Learning: State-of-The-Art and Open Challenges
Elshawi, R., Maher, M., and Sakr, S. (2019) · 1906
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AutoML: A Survey of the State-of-the-Art
He, X., Zhao, K., and Chu, X. (2019) · 1908
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Particle Swarm Optimization
Kennedy, J., and Eberhart, R. (1995) · 1948
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Some Aspects of the Sequential Design of Experiments
Robbins, H. (1952) · 1952
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Recent Advances in Finding Best Operating Conditions
Anderson, R. L. (1953) · 1953
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On Estimation of a Probability Density Function and Mode
Parzen, E. (1961) · 1961
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Kommunikation mit Automaten
Petri, C. A. (1962) · 1962
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Binary codes capable of correcting deletions, insertions, and reversals
Levenshtein, V. I. (1966) · 1966
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A General Coefficient of Similarity and Some of Its Properties
Gower, J. C. (1971) · 1971
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The estimation of the gradient of a density function, with applications in pattern recognition
Fukunaga, K., and Hostetler, L. D. (1975) · 1975
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Minimization By Random Search Techniques
Solis, F. J., and Wets, R. J.-B. (1981) · 1981
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Classification and Regression Trees
Breiman, L., Friedman, J., Stone, C. J., and Olsen, R. (1984) · 1984
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Flocks, Herds, and Schools: A Distributed Behavioral Model
Reynolds, C. W. (1987) · 1987
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Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J. (1987) · 1987
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Pattern Matching: The Gestalt Approach
Ratcliff, J. W., and Metzener, D. E. (1988) · 1988
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Genetic Programming: On the Programming of Computers by Means of Natural Selection
Koza, J. R. (1992) · 1992
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Genetic Algorithms as a Tool for Feature Selection in Machine Learning
Vafaie, H., and De Jong, K. (1992) · 1992
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Stacked Generalization
Wolpert, D. H. (1992) · 1992
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Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation
Maron, O., and Moore, A. (1993) · 1993
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Estimating attributes: Analysis and extensions of RELIEF
Kononenko, I. (1994) · 1994
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Floating search methods in feature selection
Pudil, P., Novovičová, J., and Kittler, J. (1994) · 1994
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Automatic Parameter Selection by Minimizing Estimated Error
Kohavi, R., and John, G. H. (1995) · 1995
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Genetic Programming: An Introduction
Banzhaf, W., Nordin, P., Keller, R. E., and Francone, F. D. (1997) · 1997
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Feature Selection for Classification
Dash, M., and Liu, H. (1997) · 1997
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An Economics Approach to Hard Computational Problems
Huberman, B. A., Lukose, R. M., and Hogg, T. (1997) · 1997
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A Comparative Study on Feature Selection in Text Categorization
Yang, Y., and Pedersen, J. O. (1997) · 1997
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High Performance Cluster Computing: Architectures and Systems
Buyya, R. (1999) · 1999
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Popular Ensemble Methods: An Empirical Study
Opitz, D., and Maclin, R. (1999) · 1999
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Data Preparation for Data Mining
Pyle, D. (1999) · 1999
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Meta analysis of classification algorithms for pattern recognition
Sohn, S. Y. (1999) · 1999
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AJAX:An Extensible Data Cleaning Tool
Galhardas, H., Florescu, D., Shasha, D., and Simon, E. (2000) · 2000
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Characterization of Classification Algorithms
Gama, J., and Brazdil, P. (2000) · 2000
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Data cleaning: Problems and Current Approaches
Rahm, E., and Do, H. H. (2000) · 2000
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The CRISP-DM model: the new blueprint for data mining
Shearer, C. (2000) · 2000
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Random Forests
Breiman, L. (2001) · 2001
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Potter’s Wheel: An Interactive Data Cleaning System
Raman, V., and Hellerstein, J. M. (2001) · 2001
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Choosing Multiple Parameters for Support Vector Machines
Chapelle, O., Vapnik, V., Bousquet, O., and Mukherjee, S. (2002) · 2002
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Gene Selection for Cancer Classification using Support Vector Machines
Guyon, I., Weston, J., and Barnhill, S. (2002) · 2002
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Feature generation using general constructor functions
Markovitch, S., and Rosenstein, D. (2002) · 2002
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A Pattern Search Method for Model Selection of Support Vector Regression
Momma, M., and Bennett, K. P. (2002) · 2002
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Feature Selection, Extraction and Construction
Motoda, H., and Liu, H. (2002) · 2002
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An Introduction to Variable and Feature Selection
Guyon, I., and Elisseeff, A. (2003) · 2003
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A Practical Guide to Support Vector Classification.
Hsu, C.-W., Chang, C.-C., and Lin, C.-J. (2003) · 2003
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Variable selection using SVM-based criteria
Rakotomamonjy, A. (2003) · 2003
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Model selection of SVMs using GA approach
Chen, P.-W., Wang, J.-Y., and Lee, H.-M. (2004) · 2004
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Automated Planmning: Theory & Praxis
Ghallab, M., Nau, D., and Traverso, P. (2004) · 2004
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On the Relationship Between Classical Grid Search and Probabilistic Roadmaps
LaValle, S. M., Branicky, M. S., and Lindemann, S. R. (2004) · 2004
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Gear fault detection using artificial neural networks and support vector machines with genetic algorithms
Samanta, B. (2004) · 2004
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Genetic Programming with a Genetic Algorithm for Feature Construction and Selection
Smith, M. G., and Bull, L. (2005) · 2005
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Practical Mathematical Optimization: An introduction to basic optimization theory and classical and new gradient-based algorithms
Snyman, J. A. (2005) · 2005
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A meta-learning approach to automatic kernel selection for support vector machines
Alia, S., and Smith-Miles, K. A. (2006) · 2006
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Declarative Support for Sensor Data Cleaning
Jeffery, S. R., Alonso, G., Franklin, M. J., Hong, W., and Widom, J. (2006) · 2006
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Bandit based Monte-Carlo Planning
Kocsis, L., and Szepesvári, C. (2006) · 2006
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Feature Selection With A Perceptron Neural Net
Mejía-Lavalle, M., Sucar, E., and Arroyo, G. (2006) · 2006
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Ensemble Based Systems in Decision Making
Polikar, R. (2006) · 2006
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Gaussian Processes for Machine Learning
Rasmussen, C. E., and Williams, C. K. I. (2006) · 2006
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Evolutionary Algorithms for Solving Multi-Objective Problems
Coello, C. A. C., Lamont, G. B., and Van Veldhuizen, D. A. (2007) · 2007
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Analysis of the IJCNN 2007 Agnostic Learning vs. Prior Knowledge Challenge
Guyon, I., Saffari, A., Dror, G., and Cawley, G. (2008) · 2007
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A review of feature selection techniques in bioinformatics
Saeys, Y., Inza, I., and Larrañaga, P. (2007) · 2007
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MapReduce: Simplified Data Processing on Large Clusters
Dean, J., and Ghemawat, S. (2008) · 2008
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Quantitative Data Cleaning for Large Databases
Hellerstein, J. M. (2008) · 2008
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A Field Guide to Genetic Programing
Poli, R., Langdon, W. B., McPhee, N. F., and Koza, J. R. (2008) · 2008
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Particle Swarm Model Selection for Authorship Verificatio
Escalante, H. J., Montes, M., and Luis, V. (2009) · 2009
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ParamILS: An Automatic Algorithm Configuration Framework
Hutter, F., Hoos, H. H., Leyton-Brown, K., and Stützle, T. (2009) · 2009
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Toward Provably Correct Feature Selection in Arbitrary Domains
Margaritis, D. (2009) · 2009
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Feature Construction Methods: A Survey
Sondhi, P. (2009) · 2009
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Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination
Tuv, E., Borisov, A., Runger, G., and Torkkola, K. (2009) · 2009
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Brochu, E., Cora, V. M., and de Freitas, N. (2010) · 2010
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Feature Selection as a One-Player Game
Gaudel, R., and Sebag, M. (2010) · 2010
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Maximum-Likelihood Estimation With a Contracting-Grid Search Algorithm
Hesterman, J. Y., Caucci, L., Kupinski, M. A., Barrett, H. H., and Furenlid, L. R. (2010) · 2010
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Stability selection
Meinshausen, N., and Bühlmann, P. (2010) · 2010
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Ensemble-based classifiers
Rokach, L. (2010) · 2010
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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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On the Predictive Power of Meta-Features in OpenML
Bilalli, B., Abelló, A., and Aluja-Banet, T. (2017) · 2017
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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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Advisor.
Chan, T. (2017) · 2017
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RECIPE: A Grammar-Based Framework for Automatically Evolving Classification Pipelines
de Sá, A. G. C., Pinto, W. J. G. S., Oliveira, L. O. V. B., and Pappa, G. L. (2017) · 2017
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Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F., and Kim, B. (2017) · 2017
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A design of a preprocessing framework for large database of historical documents
Messaoud, I. B., El Abed, H., Märgner, V., and Amiri, H. (2011) · 2011
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Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011) · 2011
Cited alongside, same era.
Random Search for Hyper-Parameter Optimization
Bergstra, J., and Bengio, Y. (2012) · 2012
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Stochastic Gradient Descent Tricks
Bottou, L. (2012) · 2012
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A Survey of Monte Carlo Tree Search Methods
Browne, C., Powley, E., Whitehouse, D., Lucas, S., Member, S., Cowling, P. I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., and Colton, S. (2012) · 2012
Cited alongside, same era.
An Experimental Study of the Combination of Meta-Learning with Particle Swarm Algorithms for SVM Parameter Selection
De Miranda, P. B., Prudêncio, R. B., De Carvalho, A. C. P., and Soares, C. (2012) · 2012
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Fast Automated Selection of Learning Algorithm And its Hyperparameters by Reinforcement Learning
Efimova, V., Filchenkov, A., and Shalamov, V. (2017) · 2017
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Google Vizier: A Service for Black-Box Optimization
Golovin, D., Solnik, B., Moitra, S., Kochanski, G., Karro, J., and Sculley, D. (2017) · 2017
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ExploreKit: Automatic feature generation and selection
Katz, G., Shin, E. C. R., and Song, D. (2017) · 2017
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AutoLearn - Automated Feature Generation and Selection
Kaul, A., Maheshwary, S., and Pudi, V. (2017) · 2017
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How to Build a Data Science Pipeline.
Kégl, B. (2017) · 2017
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Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J. (2017) · 2017
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One button machine for automating feature engineering in relational databases
Lam, H. T., Thiebaut, J.-M., Sinn, M., Chen, B., Mai, T., and Alkan, O. (2017) · 2017
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Bayesian Optimization for Conditional Hyperparameter Spaces
Levesque, J. C., Durand, A., Gagne, C., and Sabourin, R. (2017) · 2017
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Learning Feature Engineering for Classification
Nargesian, F., Samulowitz, H., Khurana, U., Khalil, E. B., and Turaga, D. (2017) · 2017
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HoloClean: Holistic Data Repairs with Probabilistic Inference
Rekatsinas, T., Chuy, X., Ilyasy, I. F., and Ré, C. (2017) · 2017
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Modelling multi-component predictive systems as petri nets
Salvador, M. M., Budka, M., and Gabrys, B. (2017) · 2017
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Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K., and Hassabis, D. (2017) · 2017
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FeatureHub: Towards collaborative data science
Smith, M. J., Wedge, R., and Veeramachaneni, K. (2017) · 2017
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ATM: A distributed, collaborative, scalable system for automated machine learning
Swearingen, T., Drevo, W., Cyphers, B., Cuesta-Infante, A., Ross, A., and Veeramachaneni, K. (2017) · 2017
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Automatic Frankensteining: Creating Complex Ensembles Autonomously
Wistuba, M., Schilling, N., and Schmidt-Thieme, L. (2017) · 2017
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AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
Alaa, A. M., and Van Der Schaar, M. (2018) · 2018
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A complete Machine Learning PipeLine.
Ayria, P. (2018) · 2018
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Benchmarking Automatic Machine Learning Frameworks
Balaji, A., and Allen, A. (2018) · 2018
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Autostacker: A Compositional Evolutionary Learning System
Chen, B., Wu, H., Mo, W., Chattopadhyay, I., and Lipson, H. (2018) · 2018
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Shortening Machine Learning Development Cycle with AutoML.
Clouder, A. (2018) · 2018
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Bandit-Based Automated Machine Learning
das Dôres, S. C. N., Soares, C., and Ruiz, D. (2018) · 2018
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AlphaD3M : Machine Learning Pipeline Synthesis
Drori, I., Krishnamurthy, Y., Rampin, R., Lourenco, R. d. P., Ono, J. P., Cho, K., Silva, C., and Freire, J. (2018) · 2018
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Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates
Eggensperger, K., Lindauer, M. T., Hoos, H. H., Hutter, F., and Leyton-Brown, K. (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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Practical Automated Machine Learning for the AutoML Challenge 2018
Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., and Hutter, F. (2018) · 2018
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Towards Further Automation in AutoML
Feurer, M., and Hutter, F. (2018) · 2018
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A Tutorial on Bayesian Optimization
Frazier, P. I. (2018) · 2018
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Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes
Garrido-Merchán, E. C., and Hernández-Lobato, D. (2018) · 2018
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P4ML: A Phased Performance-Based Pipeline Planner for Automated Machine Learning
Gil, Y., Yao, K.-T., Ratnakar, V., Garijo, D., Steeg, G. V., Szekely, P., Brekelmans, R., Kejriwal, M., Luo, F., and Huang, I.-H. (2018) · 2018
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Bayesian Tuning and Bandits : An Extensible , Open Source Library for AutoML by
Gustafson, L. (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. (2018) · 2018
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H2O Driverless AI.
H2O.ai (2018) · 2018
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Parallelised Bayesian Optimisation via Thompson Sampling Kirthevasan
Kandasamy, K., Krishnamurthy, A., Schneider, J., and Póczos, B. (2018) · 2018
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Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning
Koch, P., Golovidov, O., Gardner, S., Wujek, B., Griffin, J., and Xu, Y. (2018) · 2018
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Distil: A Mixed-Initiative Model Discovery System for Subject Matter Experts
Langevin, S., Jonker, D., Bethune, C., Coppersmith, G., Hilland, C., Morgan, J., Azunre, P., and Gawrilow, J. (2018) · 2018
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Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
Li, L., Jamieson, K. G., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A. (2018) · 2018
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Warmstarting of Model-based Algorithm Configuration
Lindauer, M., and Hutter, F. (2018) · 2018
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ML-Plan: Automated machine learning via hierarchical planning
Mohr, F., Wever, M., and Hüllermeier, E. (2018) · 2018
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Taking Human out of Learning Applications: A Survey on Automated Machine Learning
Quanming, Y., Mengshuo, W., Hugo, J. E., Isabelle, G., Yi-Qi, H., Yu-Feng, L., Wei-Wei, T., Qiang, Y., and Yang, Y. (2018) · 2018
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Introducing RapidMiner Auto Model.
RapidMiner (2018) · 2018
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Preprocessor Selection for Machine Learning Pipelines
Schoenfeld, B., Giraud-Carrier, C., Poggemann, M., Christensen, J., and Seppi, K. (2018) · 2018
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Hyperparameter Importance Across Datasets
van Rijn, J. N., and Hutter, F. (2018) · 2018
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LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
Weisz, G., Gyorgy, A., and Szepesvari, C. (2018) · 2018
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ML-Plan for Unlimited-Length Machine Learning Pipelines
Wever, M., Mohr, F., and Hüllermeier, E. (2018) · 2018
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How to Build a Better Machine Learning Pipeline.
Zhou, L. (2018) · 2018
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Bischl, B., Casalicchio, G., Feurer, M., Hutter, F., Lang, M., Mantovani, R. G., van Rijn, J. N., and Vanschoren, J. (2019) · 2019
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Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
Drori, I., Krishnamurthy, Y., de Paula Lourenco, R., Rampin, R., Kyunghyun, C., Silva, C., and Freire, J. (2019) · 2019
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Analysing the Overfit of the auto-sklearn Automated Machine Learning Tool
Fabris, F., and Freitas, A. A. (2019) · 2019
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An Open Source AutoML Benchmark
Gijsbers, P., LeDell, E., Thomas, J., Poirier, S., Bischl, B., and Vanschoren, J. (2019) · 2019
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Towards Human-Guided Machine Learning
Gil, Y., Honaker, J., Gupta, S., Ma, Y., Orazio, V. D., Garijo, D., Gadewar, S., Yang, Q., and Jahanshad, N. (2019) · 2019
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AI Explanations Whitepaper
Google LLC (2019) · 2019
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H2O AutoML.
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HyperparameterHunter.
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auto_ml.
Parry, P. (2019) · 2019
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Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Perrone, V., Shen, H., Seeger, M., Archambeau, C., and Jenatton, R. (2019) · 2019
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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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Automated Machine Learning with Monte-Carlo Tree Search
Rakotoarison, H., Schoenauer, M., and Sebag, M. (2019) · 2019
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Automated Machine Learning in Practice: State of the Art and Recent Results
Tuggener, L., Amirian, M., Rombach, K., Lörwald, S., Varlet, A., Westermann, C., and Stadelmann, T. (2019) · 2019
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Meta-Learning
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A System for Massively Parallel Hyperparameter Tuning
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AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model
Nguyen, T.-D., Maszczyk, T., Musial, K., Zöller, M.-A., and Gabrys, B. (2020) · 2020
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FLASH: Fast Bayesian Optimization for Data Analytic Pipelines
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