Prof. Dr. rer. nat. Marius Lindauer
Leibniz Universität Hannover
Institut für Informationsverarbeitung
Appelstr. 9A
30167 Hannover
phone: +49 511 762-5301
fax: +49 511 762-5333
office location: room 1316

Mission Statement:

In recent years, AI made impressive results in different applications possible, e.g., in computer vision, natural language processing or game playing. These breakthroughs show how AI will influence and change our daily lives in many fields. 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, such as which algorithms to apply and how to set their corresponding hyperparameters. Unfortunately, the performance and thus the success of AI systems strongly depend on these small but important design decisions. To make AI easy-to-use for more users (even for those without a strong background in AI), we develop automated machine learning (AutoML) methods, which address many different research questions, such as: (i) how to predict the best algorithm for a given input? (ii) how to efficiently search for well-performing hyperparameter settings of an algorithm? (iii) how to efficiently analyze the performance of algorithms and their inputs? Finding solutions to such problems will lead to a democratization of AI which will unleash the true potential of AI.

Short CV:

  • since Oct 2019: Professor of machine learning at the Leibniz University of Hannover
  • 2014-2019: Postdoctoral research fellow at the University of Freiburg with focus on AutoML
  • 2015: PhD in computer science at the University of Potsdam (summa cum laude) with focus on algorithm selection and configuration
  • 2013: Co-Founder of the research network COSEAL (since 2018 advisory board member of COSEAL)
  • 2010: Master of Science in computer science at the University of Potsdam
  • 2008: Bachelor of Science in computer science at the University of Potsdam
  • 2005: High school graduation (Abitur) in Berlin

Selected Awards:

  • 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)
  • David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer
    Learning Heuristic Selection with Dynamic Algorithm Configuration
    Proceedings of the 31st International Conference on Automated Planning and Scheduling {(ICAPS'21)}, August 2021
  • Theresa and Biedenkapp Eimer
    DACBench: A Benchmark Library for Dynamic Algorithm Configuration
    Proceedings of the international joint conference on artificial intelligence (IJCAI), August 2021
  • Julia Guerrero-Viu, Sven Hauns, Sergio Izquierdo, Guilherme Miotto, Simon Schrodi, Andre Biedenkapp, Thomas Elsken, Difan Deng, Marius Lindauer, Frank Hutter
    Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization
    arXiv, May 2021

Show all publications
  • Conference Contributions
    • Artur Souza, Luigi Nardi, Leonardo Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter
      Prior-guided Bayesian Optimization
      Proceedings of the Workshop on Meta-Learning (NeurIPS), pp. 1-19, December 2020
    • Berend Denkena, Marc Dittrich, Marius Lindauer, Mainka , Lukas Stürenburg
      Using AutoML to Optimize Shape Error Prediction in Milling Processes
      Proceedings of 20th Machining Innovations Conference for Aerospace Industry (MIC), December 2020
    • Gresa Shala, Andre Biedenkapp, Noor Awad, Steven Adriaensen, Marius Lindauer, Frank Hutter
      Learning Step-Size Adaptation in CMA-ES
      Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature ({PPSN}'20), September 2020
    • Theresa Eimer, Andre Biedenkapp, Frank Hutter, Marius Lindauer
      Towards Self-Paced Context Evaluations for Contextual Reinforcement Learning
      Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML'20), July 2020
    • Andre Biedenkapp, H. Furkan Bozkurt, Theresa Eimer, Frank Hutter, Marius Lindauer
      Algorithm Control: Foundation of a New Meta-Algorithmic Framework
      Proceedings of the European Conference on Artificial Intelligence (ECAI), 2020
    • M. Feurer and K. Eggensperger and S. Falkner and M. Lindauer and F. Hutter
      Practical Automated Machine Learning for the AutoML Challenge 2018
      ICML 2018 AutoML Workshop, July 2018
  • Journals
    • Lucas Zimmer, Marius Lindauer, Frank Hutter
      Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
      IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE, pp. 1-12, 2021
    • Marius Lindauer and Frank Hutter
      Best Practices for Scientific Research on Neural Architecture Search
      Journal of Machine Learning Research, Vol. 21, pp. 1-18, December 2020
  • Book Chapters
    • Hector Mendoza and Aaron Klein and Matthias Feurer and Jost Tobias Springenberg and Matthias Urban and Michael Burkart and Max Dippel and Marius Lindauer and Frank Hutter
      Towards Automatically-Tuned Deep Neural Networks
      AutoML: Methods, Sytems, Challenges, Springer, pp. 141--156, December 2018, edited by Hutter, Frank and Kotthoff, Lars and Vanschoren, Joaquin
    • M. Lindauer and H. Hoos and F. Hutter and K. Leyton-Brown
      Selection and Configuration of Parallel Portfolios
      Handbook of Parallel Constraint Reasoning, Springer, 2017, edited by Y. Hamadi and L. Sais
  • Technical Report
    • Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter
      Auto-Sklearn 2.0: The Next Generation
      arXiv:2007.04074 [cs.LG], July 2020
Other activities

Important Links:


Recently recorded talks:

Open-source projects:

  • SMAC v3: automatic tuning of hyperparameter configurations on any kind of algorithms (mainly based on Bayesian Optimization)
  • AutoPyTorch: automatic hyperparameter optimization and architecture search for deep neural networks
  • CAVE : Configuration Assessment, Visualization and Evaluation
  • Auto-Sklearn: automated machine learning toolkit