30167 Hannover
In recent years, AI achieved impressive results in different fields, incl. in computer vision, natural language processing and reinforcement learning. These breakthroughs show how AI will influence and change our daily lives, business and even research in many aspects. With the advent of deep learning and also traditional AI methods, such as AI planning, SAT solving or evolutionary algorithms, a multitude of different techniques are available these days. However, applying these techniques is challenging, and even experienced AI developers are faced with several difficult design decisions, making the development of new AI applications a tedious, error-prone and time-consuming task. Therefore, we develop new approaches to increase efficiency in AI application development by reducing the required expert knowledge, improving development time and reducing chances of error. We do this with democratization of AI and social responsibility in mind.
Research Interests
Actually, I'm interested in many topics related to AutoML, machine learning, AI and interdisciplinary applications of these. Here are some selected topics:
- Green-AutoML
- Human-centered AutoML
- Dynamic Algorithm Configuration
- Generalization of Reinforcement Learning
- Applications to production or health/medicine
Curriculum Vitae
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Working Experience
since 2022
Head of Institute of AI, Leibniz University Hannoversince 2019
Professor of Machine Learning, Leibniz University Hannover2017-2019
Lecturer (i.e., "Akademischer Rat"), University of Freiburg2014-2017
PostDoc, University of Freiburg2010-2014
Phd Student, University of Potsdam -
Education
2010-2015
Phd (Dr. rer. nat), University of Potsdam2008-2010
Master of Science, Computer Science, University of Potsdam2005-2008
Bachelor of Science, Computer Science, University of Potsdam -
Selected Awards
- 2022: ERC Starting Grant on ixAutoML
- 2020: 3rd place(*) at the official leaderboard and 1st place at the warmstart friendly leaderboard at the BBO-Challenge at NeurIPS'20 (* after fixing a minor bug)
- 2018: Winner of 2nd AutoML challenge::PAKDD2018 with aad_freibug and PoSH Auto-sklearn
- 2016: Winner of ChaLearn AutoML challenge "AutoML 5" with aad_freibug and auto-sklearn
- 2015: Winner of ICON Challenge on algorithm selection with AutoFolio (track: Par10)
- 2013: Winner of Configurable SAT Solver challenge 2013 with the Potassco team and clasp (tracks: crafted and random)
- 2012: Winner of SAT Challenge 2012 with the Potassco team and clasp (track: hard combinatorial)
- 2011: Winner of Answer Set Programming Competition with the Potassco team and claspfolio (track: NP-Problems)
- 2009: Leopold-von-Buch-Bachelor-Award (Best Bachelor in Natural Sciences 2009 at the University of Potsdam)
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Memberships
- Co-Head AutoML.org Super Group
- Advisory board member and co-founder COSEAL.net
- L3S Member
- Member and co-founder LUH Data Science Lab
- ELLIS Member
- CLAIRE supporter
- Member of Working Group 1: Technological Enablers and Data Science at Pattform Lernende Systeme
- Member of Benchmarking Network
- Member of IEEE Task Force on Automated Algorithm Design, Configuration and Selection
- Social Media
Publications
2023
Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (Accepted/In press). Contextualize Me – The Case for Context in Reinforcement Learning. Transactions on Machine Learning Research.
Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Accepted/In press). Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. In AutoML Conference 2023 PMLR.
Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Accepted/In press). Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. In GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference Companion Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO).
Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A-L., Deng, D., & Lindauer, M. (2023). Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2), [e1484].
Denkena, B., Dittrich, M-A., Noske, H., Lange, D., Benjamins, C., & Lindauer, M. (2023). Application of machine learning for fleet-based condition monitoring of ball screw drives in machine tools. The international journal of advanced
manufacturing technology.
Eimer, T., Lindauer, M., & Raileanu, R. (Accepted/In press). Hyperparameters in Reinforcement Learning and How To Tune Them. In Proceeding of the Fortieth International Conference on Machine Learning (Proceeding of the International Conference on Machine Learning).
Loni, M., Mohan, A., Asadi, M., & Lindauer, M. (Accepted/In press). Learning Activation Functions for Sparse Neural Networks. In Second International Conference on Automated Machine Learning PMLR.
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (Accepted/In press). AutoRL Hyperparameter Landscapes. In Second International Conference on Automated Machine Learning PMLR.
Ruhkopf, T., Mohan, A., Deng, D., Tornede, A., Hutter, F., & Lindauer, M. (2023). MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. Transactions on Machine Learning Research.
Segel, S., Graf, H., Tornede, A., Bischl, B., & Lindauer, M. (Accepted/In press). Symbolic Explanations for Hyperparameter Optimization. In AutoML Conference 2023 PMLR.
Theodorakopoulos, D., Manß, C., Stahl, F., & Lindauer, M. (2023). Green-AutoML for Plastic Litter Detection. In Proceedings of the ICLR Workshop on Tackling Climate Change with Machine Learning
Zöller, M., Mauthe, F., Zeiler, P., Lindauer, M., & Huber, M. (2023). Automated Machine Learning for Remaining Useful Life Predictions. In Proceedings of the international conference on Systems Science and Engineering, Human-Machine Systems, and Cybernetics (IEEE SMC) IEEE Xplore Digital Library.
2022
Adriaensen, S., Biedenkapp, A., Shala, G., Awad, N., Eimer, T., Lindauer, M., & Hutter, F. (2022). Automated Dynamic Algorithm Configuration. Journal of Artificial Intelligence Research, 75.
doi.org/10.48550/arXiv.2205.13881
,Benjamins, C., Raponi, E., Jankovic, A., Blom, K. V. D., Santoni, M. L., Lindauer, M., & Doerr, C. (2022). PI is back! Switching Acquisition Functions in Bayesian Optimization. In 2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems
Benjamins, C., Jankovic, A., Raponi, E., Blom, K. V. D., Lindauer, M., & Doerr, C. (2022). Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. In 6th Workshop on Meta-Learning at NeurIPS 2022
Bothmann, L., Strickroth, S., Casalicchio, G., Rügamer, D., Lindauer, M., Scheipl, F., & Bischl, B. (2022). Developing Open Source Educational Resources for Machine Learning and Data Science. In Teaching Machine Learning Workshop at ECML 2022
Deng, D., Karl, F., Hutter, F., Bischl, B., & Lindauer, M. (2022). Efficient Automated Deep Learning for Time Series Forecasting. In Proceedings of the European Conference on Machine Learning (ECML)
Deng, D., & Lindauer, M. (2022). Searching in the Forest for Local Bayesian Optimization. In ECML/PKDD workshop on Meta-learning
Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M. T., & Hutter, F. (2022). Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning. Journal of Machine Learning Research, 23.
Hvarfner, C., Stoll, D., Souza, A. L. F., Lindauer, M., Hutter, F., & Nardi, L. (Accepted/In press). π BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. In Proceedings of the International conference on Learning Representation (ICLR)
