Optimization and Automated Machine Learning

Can we use Machine Learning techniques to improve Machine Learning processes themselves? Automated Machine Learning (AutoML) is about removing (some of) the human element from choosing ML parameters and methods. This gives rise to a difficult optimization problem where a single performance evaluation can take a long time, so fast convergence is desirable. Our group is therefore dealing with the following questions:

Members

Name       Position
Dr. Janek Thomas       PostDoc
Dr. Michel Lang       PostDoc
Florian Karl       PhD Student
Florian Pfisterer       PhD Student
Julia Moosbauer       PhD Student
Katharina Rath       PhD Student
Lennart Schneider       PhD Student
Martin Binder       PhD Student
Philipp Müller       PhD Student
Stefan Coors       PhD Student
Tobias Pielok       PhD Student

Projects and Software

Publications

  1. Rügamer D, Bender A, Wiegrebe S et al. (2022) Factorized Structured Regression for Large-Scale Varying Coefficient Models Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), Springer International Publishing.
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  2. Rügamer D (2022) Additive Higher-Order Factorization Machines. arXiv preprint arXiv:2205.14515.
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  3. Gijsbers P, Bueno MLP, Coors S et al. (2022) AMLB: an AutoML Benchmark. arXiv preprint arXiv:2207.12560.
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  4. Karl F, Pielok T, Moosbauer J et al. (2022) Multi-Objective Hyperparameter Optimization – An Overview. arXiv preprint arXiv:2206.07438.
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  5. Schneider L, Pfisterer F, Thomas J, Bischl B (2022) A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 2136–2142. Association for Computing Machinery, New York, NY, USA.
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  6. Pargent F, Pfisterer F, Thomas J, Bischl B (2022) Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. Computational Statistics, 1–22.
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  7. Schneider L, Pfisterer F, Kent P, Branke J, Bischl B, Thomas J (2022) Tackling Neural Architecture Search With Quality Diversity Optimization First Conference on Automated Machine Learning (Main Track),
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  8. Pfisterer F, Schneider L, Moosbauer J, Binder M, Bischl B (2022) YAHPO Gym – An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization First Conference on Automated Machine Learning (Main Track),
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  9. *Coors S, *Schalk D, Bischl B, Rügamer D (2021) Automatic Componentwise Boosting: An Interpretable AutoML System. ECML-PKDD Workshop on Automating Data Science.
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  10. Bischl B, Binder M, Lang M et al. (2021) Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. arXiv preprint arXiv:2107.05847.
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  11. Gijsbers P, Pfisterer F, Rijn JN van, Bischl B, Vanschoren J (2021) Meta-Learning for Symbolic Hyperparameter Defaults. 2021 Genetic and Evolutionary Computation Conference Companion (GECCO ’21 Companion).
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  12. Gerostathopoulos I, Plášil F, Prehofer C, Thomas J, Bischl B (2021) Automated Online Experiment-Driven Adaptation–Mechanics and Cost Aspects. IEEE Access 9, 58079–58087.
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  13. Kaminwar SR, Goschenhofer J, Thomas J, Thon I, Bischl B (2021) Structured Verification of Machine Learning Models in Industrial Settings. Big Data.
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  14. Moosbauer J, Binder M, Schneider L et al. (2021) Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers.
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  15. Binder M, Pfisterer F, Lang M, Schneider L, Kotthoff L, Bischl B (2021) mlr3pipelines - Flexible Machine Learning Pipelines in R. Journal of Machine Learning Research 22, 1–7.
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  16. Schneider L, Pfisterer F, Binder M, Bischl B (2021) Mutation is all you need 8th ICML Workshop on Automated Machine Learning,
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  17. Binder M, Pfisterer F, Bischl B (2020) Collecting Empirical Data About Hyperparameters for Data Driven AutoML AutoML Workshop at ICML 2020,
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  18. Binder M, Moosbauer J, Thomas J, Bischl B (2020) Multi-Objective Hyperparameter Tuning and Feature Selection Using Filter Ensembles Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pp. 471–479. Association for Computing Machinery, New York, NY, USA.
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  19. Bommert A, Sun X, Bischl B, Rahnenführer J, Lang M (2020) Benchmark for filter methods for feature selection in high-dimensional classification data. Computational Statistics & Data Analysis 143, 106839.
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  20. Sun X, Bommert A, Pfisterer F, Rähenfürher J, Lang M, Bischl B (2020) High Dimensional Restrictive Federated Model Selection with Multi-objective Bayesian Optimization over Shifted Distributions. In: In: Bi Y , In: Bhatia R , In: Kapoor S (eds) Intelligent Systems and Applications, pp. 629–647. Springer International Publishing, Cham.
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  21. Ellenbach N, Boulesteix A-L, Bischl B, Unger K, Hornung R (2020) Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning. Journal of Classification, 1–20.
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  22. Pfisterer F, Thomas J, Bischl B (2019) Towards Human Centered AutoML. arXiv preprint arXiv:1911.02391.
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  23. Pfisterer F, Beggel L, Sun X, Scheipl F, Bischl B (2019) Benchmarking time series classification – Functional data vs machine learning approaches. arXiv preprint arXiv:1911.07511.
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  24. Pfisterer F, Coors S, Thomas J, Bischl B (2019) Multi-Objective Automatic Machine Learning with AutoxgboostMC. arXiv preprint arXiv:1908.10796.
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  25. Sun X, Lin J, Bischl B (2019) ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement Learning. CoRR abs/1904.05381.
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  26. Probst P, Boulesteix A-L, Bischl B (2019) Tunability: Importance of Hyperparameters of Machine Learning Algorithms. Journal of Machine Learning Research 20, 1–32.
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  27. Gijsbers P, LeDell E, Thomas J, Poirier S, Bischl B, Vanschoren J (2019) An Open Source AutoML Benchmark. CoRR abs/1907.00909.
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  28. Schüller N, Boulesteix A-L, Bischl B, Unger K, Hornung R (2019) Improved outcome prediction across data sources through robust parameter tuning. 221.
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  29. Rijn JN van, Pfisterer F, Thomas J, Bischl B, Vanschoren J (2018) Meta Learning for Defaults–Symbolic Defaults NeurIPS 2018 Workshop on Meta Learning,
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  30. Kühn D, Probst P, Thomas J, Bischl B (2018) Automatic Exploration of Machine Learning Experiments on OpenML. arXiv preprint arXiv:1806.10961.
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  31. Thomas J, Coors S, Bischl B (2018) Automatic Gradient Boosting. ICML AutoML Workshop.
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  32. Bischl B, Richter J, Bossek J, Horn D, Thomas J, Lang M (2017) mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions. arXiv preprint arXiv:1703.03373.
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  33. Kotthaus H, Richter J, Lang A et al. (2017) RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization International Conference on Learning and Intelligent Optimization, pp. 180–195. Springer.
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