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 101 - 108 von 108

2014


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

2013


Gebser, M., Jost, H., Kaminski, R., Obermeier, P., Sabuncu, O., Schaub, T., & Schneider, M. (2013). Ricochet Robots: A Transverse ASP Benchmark. In LPNMR (S. 348-360) https://doi.org/10.1007/978-3-642-40564-8_35
Hoos, H. H., Kaufmann, B., Schaub, T., & Schneider, M. (2013). Robust Benchmark Set Selection for Boolean Constraint Solvers. In LION (S. 138-152) https://doi.org/10.1007/978-3-642-44973-4_16

2012


Hoos, H. H., Kaminski, R., Schaub, T., & Schneider, M. (2012). aspeed: ASP-based Solver Scheduling. In ICLP (Technical Communications) (S. 176-187) https://doi.org/10.4230/LIPIcs.ICLP.2012.176
Schneider, M., & Hoos, H. H. (2012). Quantifying Homogeneity of Instance Sets for Algorithm Configuration. In LION (S. 190-204) https://doi.org/10.1007/978-3-642-34413-8_14
Silverthorn, B., Lierler, Y., & Schneider, M. (2012). Surviving Solver Sensitivity: An ASP Practitioner's Guide. In ICLP (Technical Communications) (S. 164-175) https://doi.org/10.4230/LIPIcs.ICLP.2012.164

2011


Gebser, M., Kaufmann, B., Kaminski, R., Ostrowski, M., Schaub, T., & Schneider, M. (2011). Potassco: The Potsdam Answer Set Solving Collection. AI Commun., 24(2), 107-124. Artikel 2. https://doi.org/10.3233/AIC-2011-0491
Möller, M., Schneider, M., Wegner, M., & Schaub, T. (2011). Centurio, a General Game Player: Parallel, Java- and ASP-based. Künstliche Intell., 25(1), 17-24. Artikel 1. https://doi.org/10.1007/s13218-010-0077-4