Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms

By Chris J. Thornton, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown. 2012

1,705 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Neural Architecture Search & AutoML

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In Artificial Intelligence

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In Neural Architecture Search & AutoML

Citations in the corpus

AutoHAS: Differentiable Hyper-parameter and Architecture Search (2020, methodology): “They employ a surrogate model and an acquisition function to decide which candidate to evaluate next [49].”
Multi-Fidelity Automatic Hyper-Parameter Tuning via Transfer Series Expansion (2019, methodology): “Further more, some open-source tools (Feurer et al. 2015; Thornton et al. 2013) based on those methods make application of AutoML easier.”
Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “The sister package Auto-WEKA (Thornton et al., 2013; Kotthoff et al., 2016) provides very similar functionality for the WEKA library.”
Survey on Automated Machine Learning (2019, methodology): “Starting from 2011, many different methods of applying Bayesian optimization for hyperparameter tuning (Bergstra et al., 2011; Snoek et al., 2012) and model selection (Thornton et al., 2013) have been proposed.”
Efficient Benchmarking of Hyperparameter Optimizers via Surrogates (2015, methodology): “Recently, sequential model-based Bayesian optimization methods have been shown to outperform more traditional methods for this problem (such as grid search and random search) and to rival— and in some cases surpass—human domain experts in finding good hyperparameter settings (Snoek, Larochelle, and ”
Raiders of the Lost Architecture: Kernels for Bayesian Optimization in Conditional Parameter Spaces (2014, background): “, in deep neural net works [7]; flexible computer vision architectures [8]; and the combined selection and hyperparam eter optimization of machine learning algorithms [9].”
Initializing Bayesian Hyperparameter Optimization via Meta-Learning (2015, methodology): “Here, we evaluated MI-SMBO on small and mediumsized hyperparameter optimiziation problems, and an important open research question is to extend it to even larger configuration spaces, such as those of Auto-WEKA (Thornton et al. 2013) and Hyperopt-Sklearn (Komer, Bergstra, and Eliasmith 2014).”
Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn (2014, background): “The Auto-Weka project (Thornton et al., 2013) was the first to show that an entire library of machine learning approaches (Weka (Hall et al.”
Automated Machine Learning on Big Data using Stochastic Algorithm Tuning (2014, methodology): “BO has been employed successfully in the configuration of deep neural network hyperparameters [4, 2], a complex vision architecture [5], and in Auto-WEKA [6], a framework incorporating a diverse range of classification algorithms.”
Tune: A Research Platform for Distributed Model Selection and Training (2018, background): “Further, both Auto-SKLearn and Auto-WEKA are tied to Scikit-Learn and WEKA respectively as the only machine learning frameworks supported.”
Sequential Model-Based Ensemble Optimization (2014, methodology): “To address these limitations, there has been an increasing amount of work on automatic hyperparameter optimization [Bergstra et al., 2011, Hutter et al., 2011, Snoek et al., 2012, Thornton et al., 2013].”
Automated Machine Learning: From Principles to Practices (2018, methodology): “In this section, the transformation of search space is mainly divided into four classes: softmax, sparse coding, autoencoder and stochastic relaxation.”
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search (2019, positive): “Bayesian Optimization (BO) is a popular method for the hyper-parameter search [12, 36, 41, 14]; It is proven to be an effective black-box optimization technique for small scale problems, e.g. ﬁnding good hyper-parameters for Stochastic Gradient Descent (SGD).”
Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization (2015, methodology): “Since the hyperparameter tuning often decides whether the performance of an algorithm is state of the art or just moderate, the task of hyperparameter optimization is as important as developing new models [2,7,20,22,25].”
Auto-Keras: Efficient Neural Architecture Search with Network Morphism (2018, methodology): “Similar to SMAC (Hutter et al., 2011), Auto-WEKA (Thornton et al., 2013), and Auto-Sklearn (Feurer et al., 2015), the goal is to enable domain experts who are not familiar with machine learning technologies to use the machine learning techniques easily.”