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We study the problem of using low computational cost to automate the choices of learners and hyperparameters for an ad-hoc training dataset and error metric, by conducting trials of different configurations on the given training data.
An open source automl benchmark
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Access path selection in a relational database management system
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A study of cross-validation and bootstrap for accuracy estimation and model selection
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The Elements of Statistical Learning
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Sequential model-based optimization for general algorithm configuration
Hutter, F., Hoos, H. H., and Leyton-Brown, K · 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
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Synopses for massive data: Samples, histograms, wavelets, sketches
Cormode, G., Garofalakis, M., Haas, P. J., and Jermaine, C · 2012
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Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P · 2012
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Openml: Networked science in machine learning
Vanschoren, J., van Rijn, J. N., Bischl, B., and Torgo, L · 2014
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Efficient and robust automated machine learning
Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., and Hutter, F · 2015
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Automating biomedical data science through tree-based pipeline optimization
Olson, R. S., Urbanowicz, R. J., Andrews, P. C., Lavender, N. A., Kidd, L. C., and Moore, J. H · 2016
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A · 2017
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Pmlb: a large benchmark suite for machine learning evaluation and comparison
Olson, R. S., La Cava, W., Orzechowski, P., Urbanowicz, R. J., and Moore, J. H · 2017
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Statistics in Microsoft SQL Server 2017
SQL Server docs · 2017
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BOHB: Robust and efficient hyperparameter optimization at scale
Falkner, S., Klein, A., and Hutter, F · 2018
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Probabilistic matrix factorization for automated machine learning
Fusi, N., Sheth, R., and Elibol, M · 2018
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The case for learned index structures
Kraska, T., Beutel, A., Chi, E. H., Dean, J., and Polyzotis, N · 2018
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Query-based workload forecasting for self-driving database management systems
Ma, L., Aken, D. V., Hefny, A., Mezerhane, G., Pavlo, A., and Gordon, G. J · 2018
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Selectivity estimation for range predicates using lightweight models
Dutt, A., Wang, C., Nazi, A., Kandula, S., Narasayya, V. R., and Chaudhuri, S · 2019
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Plan-structured deep neural network models for query performance prediction
Marcus, R. C. and Papaemmanouil, O · 2019
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Practical Automated Machine Learning on Azure: Using Azure Machine Learning to Quickly Build AI Solutions
Mukunthu, D., Shah, P., and Tok, W · 2019
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Continuous integration of machine learning models with ease.ml/ci: Towards a rigorous yet practical treatment
Renggli, C., Karlaš, B., Ding, B., Liu, F., Wu, W., and Zhang, C · 2019
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Democratizing data science through interactive curation of ml pipelines
Shang, Z., Zgraggen, E., Buratti, B., Kossmann, F., Eichmann, P., Chung, Y., Binnig, C., Upfal, E., and Kraska, T · 2019
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Cloudy with high chance of dbms: A 10-year prediction for enterprise-grade ml
Agrawal, A., Chatterjee, R., Curino, C., Floratou, A., Gowdal, N., Interlandi, M., Jindal, A., Karanasos, K., Krishnan, S., Kroth, B., et al · 2020
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Neural architecture search: A survey
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Fiting-tree: A data-aware index structure
Galakatos, A., Markovitch, M., Binnig, C., Fonseca, R., and Kraska, T · 2019
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H2o automl
H2O.ai · 2019
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The autofeat python library for automatic feature engineering and selection
Horn, F., Pack, R., and Rieger, M · 2019
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Efficient identification of approximate best configuration of training in large datasets
Huang, S., Wang, C., Ding, B., and Chaudhuri, S · 2019
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Learned cardinalities: Estimating correlated joins with deep learning
Kipf, A., Kipf, T., Radke, B., Leis, V., Boncz, P., and Kemper, A · 2019
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Efficiently approximating selectivity functions using low overhead regression models
Dutt, A., Wang, C., Narasayya, V., and Chaudhuri, S · 2020
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Autogluon-tabular: Robust and accurate automl for structured data
Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., and Smola, A · 2020
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Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., and Hutter, F · 2020
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A system for massively parallel hyperparameter tuning
Li, L., Jamieson, K., Rostamizadeh, A., Gonina, E., Ben-tzur, J., Hardt, M., Recht, B., and Talwalkar, A · 2020
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Elastic machine learning algorithms in amazon sagemaker
Liberty, E., Karnin, Z., Xiang, B., Rouesnel, L., Coskun, B., Nallapati, R., Delgado, J., Sadoughi, A., Astashonok, Y., Das, P., et al · 2020
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Deep double descent: Where bigger models and more data hurt
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Umbra: A disk-based system with in-memory performance
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Frugal optimization for cost-related hyperparameters
Wu, Q., Wang, C., and Huang, S · 2021
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