AutoML


The sub-field of optimisation that specifically aims to automate model selection in machine learning. (and also occasionally ensemble construction)

There are two major approaches here that I am aware of, both of which are related in a kind of abstract way, but which are in practice different

  1. Finding the right architecture for your nueral net, a.k.a architecture search
  2. Hyperparameter optimisation which I have made into a separate notebook.

The first one I might cover here.

TODO: work out if this is the same as “meta learning”? I think not; I suspect that of being transfer learning.

Reinforcement learning approaches

Quoc Le & Barret Zoph discuss using reinforcement learning to learn neural models:

Typically, our machine learning models are painstakingly designed by a team of engineers and scientists. This process of manually designing machine learning models is difficult because the search space of all possible models can be combinatorially large — a typical 10-layer network can have ~1010 candidate networks! […]

To make this process of designing machine learning models much more accessible, we’ve been exploring ways to automate the design of machine learning models. […] in this blog post, we’ll focus on our reinforcement learning approach and the early results we’ve gotten so far.

In our approach (which we call “AutoML”), a controller neural net can propose a “child” model architecture, which can then be trained and evaluated for quality on a particular task. That feedback is then used to inform the controller how to improve its proposals for the next round.

Implementations

auto-sklearn

  • auto-sklearn, The implementation of hyperparameter optimization by Feurer et al. (2015):

    auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator:

    import autosklearn.classification
    cls = autosklearn.classification.AutoSklearnClassifier()
    cls.fit(X_train, y_train)
    predictions = cls.predict(X_test)

    auto-sklearn frees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction.

Abdel-Gawad, Ahmed, and Simon Ratner. 2007. “Adaptive Optimization of Hyperparameters in L2-Regularised Logistic Regression.” http://cs229.stanford.edu/proj2007/AbdelGawadRatner-AdaptiveHyperparameterOptimization.pdf.

Bengio, Yoshua. 2000. “Gradient-Based Optimization of Hyperparameters.” Neural Computation 12 (8): 1889–1900. https://doi.org/10.1162/089976600300015187.

Bergstra, James S., Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. 2011. “Algorithms for Hyper-Parameter Optimization.” In Advances in Neural Information Processing Systems, 2546–54. Curran Associates, Inc. http://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.

Bergstra, J, D Yamins, and D D Cox. 2013. “Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures.” In ICML, 9.

Domke, Justin. 2012. “Generic Methods for Optimization-Based Modeling.” In International Conference on Artificial Intelligence and Statistics, 318–26. http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2012_Domke12.pdf.

Eggensperger, Katharina, Matthias Feurer, Frank Hutter, James Bergstra, Jasper Snoek, Holger H. Hoos, and Kevin Leyton-Brown. n.d. “Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters.” Accessed August 21, 2017. http://www.automl.org/papers/13-BayesOpt_EmpiricalFoundation.pdf.

Eigenmann, R., and J. A. Nossek. 1999. “Gradient Based Adaptive Regularization.” In Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468), 87–94. https://doi.org/10.1109/NNSP.1999.788126.

Feurer, Matthias, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter. 2015. “Efficient and Robust Automated Machine Learning.” In Advances in Neural Information Processing Systems 28, edited by C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, 2962–70. Curran Associates, Inc. http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf.

Foo, Chuan-sheng, Chuong B. Do, and Andrew Y. Ng. 2008. “Efficient Multiple Hyperparameter Learning for Log-Linear Models.” In Advances in Neural Information Processing Systems 20, edited by J. C. Platt, D. Koller, Y. Singer, and S. T. Roweis, 377–84. Curran Associates, Inc. http://papers.nips.cc/paper/3286-efficient-multiple-hyperparameter-learning-for-log-linear-models.pdf.

Fu, Jie, Hongyin Luo, Jiashi Feng, Kian Hsiang Low, and Tat-Seng Chua. 2016. “DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks.” In PRoceedings of IJCAI, 2016. http://arxiv.org/abs/1601.00917.

Gelbart, Michael A., Jasper Snoek, and Ryan P. Adams. 2014. “Bayesian Optimization with Unknown Constraints.” In Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 250–59. UAI’14. Arlington, Virginia, United States: AUAI Press. http://hips.seas.harvard.edu/files/gelbart-constrained-uai-2014.pdf.

Grünewälder, Steffen, Jean-Yves Audibert, Manfred Opper, and John Shawe-Taylor. 2010. “Regret Bounds for Gaussian Process Bandit Problems.” In, 9:273–80. https://hal-enpc.archives-ouvertes.fr/hal-00654517/document.

Hutter, Frank, Holger H. Hoos, and Kevin Leyton-Brown. 2011. “Sequential Model-Based Optimization for General Algorithm Configuration.” In Learning and Intelligent Optimization, 6683:507–23. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25566-3_40.

Hutter, Frank, Holger Hoos, and Kevin Leyton-Brown. 2013. “An Evaluation of Sequential Model-Based Optimization for Expensive Blackbox Functions.” In Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, 1209–16. GECCO ’13 Companion. New York, NY, USA: ACM. https://doi.org/10.1145/2464576.2501592.

Li, Lisha, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar. 2016. “Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization,” March. http://arxiv.org/abs/1603.06560.

Liu, Hanxiao, Karen Simonyan, and Yiming Yang. 2018. “DARTS: Differentiable Architecture Search,” June. http://arxiv.org/abs/1806.09055.

Maclaurin, Dougal, David K. Duvenaud, and Ryan P. Adams. 2015. “Gradient-Based Hyperparameter Optimization Through Reversible Learning.” In ICML, 2113–22. http://www.jmlr.org/proceedings/papers/v37/maclaurin15.pdf.

Močkus, J. 1975. “On Bayesian Methods for Seeking the Extremum.” In Optimization Techniques IFIP Technical Conference, edited by Prof Dr G. I. Marchuk, 400–404. Lecture Notes in Computer Science. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-38527-2_55.

Real, Esteban, Chen Liang, David R. So, and Quoc V. Le. 2020. “AutoML-Zero: Evolving Machine Learning Algorithms from Scratch,” March. https://arxiv.org/abs/2003.03384v1.

Salimans, Tim, Diederik Kingma, and Max Welling. 2015. “Markov Chain Monte Carlo and Variational Inference: Bridging the Gap.” In Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 1218–26. ICML’15. Lille, France: JMLR.org. http://proceedings.mlr.press/v37/salimans15.html.

Snoek, Jasper, Hugo Larochelle, and Ryan P. Adams. 2012. “Practical Bayesian Optimization of Machine Learning Algorithms.” In Advances in Neural Information Processing Systems, 2951–9. Curran Associates, Inc. http://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.

Snoek, Jasper, Kevin Swersky, Rich Zemel, and Ryan Adams. 2014. “Input Warping for Bayesian Optimization of Non-Stationary Functions.” In Proceedings of the 31st International Conference on Machine Learning (ICML-14), 1674–82. http://www.jmlr.org/proceedings/papers/v32/snoek14.pdf.

Srinivas, Niranjan, Andreas Krause, Sham M. Kakade, and Matthias Seeger. 2012. “Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design.” IEEE Transactions on Information Theory 58 (5): 3250–65. https://doi.org/10.1109/TIT.2011.2182033.

Swersky, Kevin, Jasper Snoek, and Ryan P Adams. 2013. “Multi-Task Bayesian Optimization.” In Advances in Neural Information Processing Systems 26, edited by C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, 2004–12. Curran Associates, Inc. http://papers.nips.cc/paper/5086-multi-task-bayesian-optimization.pdf.

Thornton, Chris, Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. 2013. “Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms.” In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 847–55. KDD ’13. New York, NY, USA: ACM. https://doi.org/10.1145/2487575.2487629.