Institut
Marius Lindauer
Prof. Dr. rer. nat. Marius Lindauer
Adresse
Welfengarten 1
30167 Hannover
Gebäude
Raum
Prof. Dr. rer. nat. Marius Lindauer
Adresse
Welfengarten 1
30167 Hannover
Gebäude
Raum

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

Publications

Zeige Ergebnisse 81 - 97 von 97

2017


Hutter, F., Lindauer, M., Balint, A., Bayless, S., Hoos, H., & Leyton-Brown, K. (2017). The Configurable SAT Solver Challenge (CSSC). Artificial intelligence, 243, 1-25. https://doi.org/10.1016/j.artint.2016.09.006
Lindauer, M., Hutter, F., Hoos, H. H., & Schaub, T. (2017). AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract). in C. Sierra (Hrsg.), International Joint Conference on Artificial Intelligence (IJCAI 2017) (S. 5025-5029). AAAI Press/International Joint Conferences on Artificial Intelligence. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjo2uHc_87qAhVpzMQBHf2lDTwQFjABegQIAxAB&url=https%3A%2F%2Fwww.ijcai.org%2FProceedings%2F2017%2F0715.pdf&usg=AOvVaw1ART0bWLbCU4uLc4oV19yv
Lindauer, M. T., van Rijn, J. N., & Kotthoff, L. (2017). Open Algorithm Selection Challenge 2017 Setup and Scenarios. http://proceedings.mlr.press/v79/lindauer17a/lindauer17a.pdf
Wagner, M., Friedrich, T., & Lindauer, M. (2017). Improving local search in a minimum vertex cover solver for classes of networks. in 2017 IEEE Congress on Evolutionary Computation (CEC): Proceedings (S. 1704-1711). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/cec.2017.7969507

2016


Bischl, B., Kerschke, P., Kotthoff, L., Lindauer, M., Malitsky, Y., Fréchette, A., Hoos, H., Hutter, F., Leyton-Brown, K., Tierney, K., & Vanschoren, J. (2016). ASlib: A benchmark library for algorithm selection. Artificial intelligence, 237, 41-58. https://doi.org/10.1016/j.artint.2016.04.003
Lindauer, M., Bergdoll, R. D., & Hutter, F. (2016). An Empirical Study of Per-instance Algorithm Scheduling. in P. Festa, M. Sellmann, & J. Vanschoren (Hrsg.), Learning and Intelligent Optimization (S. 253-259). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10079 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-50349-3_20
Lindauer, M., Hoos, H., Leyton-Brown, K., & Schaub, T. (2016). Automatic construction of parallel portfolios via algorithm configuration. Artificial intelligence, 244, 272-290. https://doi.org/10.1016/j.artint.2016.05.004
Manthey, N., & Lindauer, M. (2016). SpyBug: Automated Bug Detection in the Configuration Space of SAT Solvers. in D. Le Berre, & N. Creignou (Hrsg.), Theory and Applications of Satisfiability Testing – SAT 2016 (S. 554-561). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9710). Springer Verlag. https://doi.org/10.1007/978-3-319-40970-2_36

2015


Albrecht, S. V., Beck, J. C., Buckeridge, D. L., Botea, A., Caragea, C., Chi, C. H., Damoulas, T., Dilkina, B., Eaton, E., Fazli, P., Ganzfried, S., Giles, C. L., Guillet, S., Holte, R., Hutter, F., Koch, T., Leonetti, M., Lindauer, M., Machado, M. C., ... Zheng, Y. (2015). Reports on the 2015 AAAI Workshop Series. AI magazine, 36(2), 90-101. https://doi.org/10.1609/aimag.v36i2.2590
Falkner, S., Lindauer, M., & Hutter, F. (2015). SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers. in M. Heule, & S. Weaver (Hrsg.), Theory and Applications of Satisfiability Testing – SAT 2015 (S. 215-222). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9340). Springer Verlag. https://doi.org/10.1007/978-3-319-24318-4_16
Hutter, F., Lindauer, M., & Malitsky, Y. (2015). Preface. in Algorithm configuration: papers presented at the Twenty-Ninth AAAI Conference on Artificial Intelligence (S. vii). (AAAI Workshop - Technical Report).
Lindauer, M., Hoos, H. H., Schaub, T., & Hutter, F. (2015). Auto folio: Algorithm configuration for algorithm selection. in Algorithm Configuration: papers presented at the Twenty-Ninth AAAI Conference on Artificial Intelligence (S. 9-15). (AAAI Workshop - Technical Report). AI Access Foundation.
Lindauer, M. T., Hoos, H., Hutter, F., & Schaub, T. (2015). AutoFolio: An Automatically Configured Algorithm Selector. Journal of Artificial Intelligence Research, 53, 745-778. https://doi.org/10.1613/jair.4726
Lindauer, M., Hoos, H. H., & Hutter, F. (2015). From Sequential Algorithm Selection to Parallel Portfolio Selection. in C. Dhaenens, L. Jourdan, & M-E. Marmion (Hrsg.), Learning and Intelligent Optimization (S. 1-16). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 8994). Springer Verlag. https://doi.org/10.1007/978-3-319-19084-6_1

2014


Hoos, H., Kaminski, R., Lindauer, M., & Schaub, T. (2014). aspeed: Solver scheduling via answer set programming. Theory and Practice of Logic Programming, 15(1), 117-142. https://doi.org/10.1017/s1471068414000015
Hoos, H., Lindauer, M., & Schaub, T. (2014). claspfolio 2: Advances in Algorithm Selection for Answer Set Programming. Theory and Practice of Logic Programming, 14(4-5), 569-585. https://doi.org/10.1017/S1471068414000210
Hutter, F., López-Ibáñez, M., Fawcett, C., Lindauer, M., Hoos, H. H., Leyton-Brown, K., & Stützle, T. (2014). AClib: A Benchmark Library for Algorithm Configuration. in Learning and Intelligent Optimization (S. 36-40). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 8426 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-09584-4_4