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

After having nearly 3 awesome years at the TNT, I moved to the institute of AI on July 1st 2022. Please check out my new website.

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.

Current Research Foci (2022) and Projects
Current research focus and projects (as of 2022)


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)
  • René Sass, Eddie Bergman, André Biedenkapp, Frank Hutter, Marius Lindauer
    DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning
    Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML@ICML’22), p. 6, June 2022
  • Tim Ruhkopf Aditya Mohan
    Towards Meta-learned Algorithm Selection using Implicit Fidelity Information
    ArXiv Preprint, June 2022
  • Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer
    POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning
    Arxiv Preprint, May 2022

Show all publications
  • Conference Contributions
    • Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
      Explaining Hyperparameter Optimization via Partial Dependence Plots
      Proceedings of the international conference on Neural Information Processing Systems (NeurIPS), December 2021
    • Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka
      Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data
      Proceedings of the international conference on Neural Information Processing Systems (NeurIPS), December 2021
    • Artur Souza, Luigi Nardi, Leonardo Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter
      Bayesian Optimization with a Prior for the Optimum
      Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), September 2021
    • Andre Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer
      TempoRL: Learning When to Act
      Proceedings of the international conference on machine learning (ICML), July 2021
    • Theresa Eimer, Andre Biedenkapp, Frank Hutter, Marius Lindauer
      Self-Paced Context Evaluation for Contextual Reinforcement Learning
      Proceedings of the international conference on machine learning (ICML), July 2021
    • Berend Denkena, Marc Dittrich, Marius Lindauer, Julia 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
    • 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
  • Journals
    • Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter
      SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
      Journal of Machine Learning Research (JMLR) -- MLOSS, Vol. 23, No. 54, pp. 1-9, January 2022
    • 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, Vol. 43, No. 9, pp. 3079 - 3090, August 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
  • 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:


Selection of Recorded Talks:

Selection of 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