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Prof. Dr. rer. nat. Marius Lindauer
Leibniz Universität Hannover
Institut für Informationsverarbeitung
Appelstr. 9A
30167 Hannover
Germany
phone: +49 511 762-5301
fax: +49 511 762-5333
office location: room 1306

Short CV:

  • since Oct 2019: Professor for 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:

  • 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)
Show recent publications only
  • 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
  • Journals
    • K. Eggensperger and M. Lindauer and F. Hutter
      Pitfalls and Best Practices in Algorithm Configuration
      Journal of Artificial Intelligence Research (JAIR), Vol. 64, pp. 861-893, 2019
    • K. Eggensperger andM. Lindauer andH. Hoos andF. Hutter andK Leyton-Brown
      Efficient Benchmarking of Algorithm Configurators via Model-Based Surrogates
      Machine Learning, Vol. 107, pp. 15-41, 2018
    • M. Lindauer and H. Hoos and K. Leyton-Brown and T. Schaub
      Automatic Construction of Parallel Portfolios via Algorithm Configuration
      Artificial Intelligence Journal (AIJ), Vol. 244, pp. 272-290, March 2017
    • M. Wagner and M. Lindauer and M. Misir and S. Nallaperuma and F. Hutter
      A case study of algorithm selection for the traveling thief problem
      Journal of Heuristics, pp. 1-26, March 2017
    • F. Hutter and M. Lindauer and A. Balint and S. Bayless and H. Hoos and K. Leyton-Brown
      The Configurable SAT Solver Challenge (CSSC)
      Artificial Intelligence Journal (AIJ), Vol. 243, pp. 1-25, February 2017
    • B. Bischl and P. Kerschke and L. Kotthoff and M. Lindauer and Y. Malitsky and A. Frech\'ette and H. Hoos and F. Hutter and K. Leyton-Brown and K. Tierney and J. Vanschoren
      ASlib: A Benchmark Library for Algorithm Selection
      Artificial Intelligence Journal (AIJ), Vol. 237, pp. 41-58, August 2016
    • M. Lindauer and H. Hoos and F. Hutter and T. Schaub
      AutoFolio: An automatically configured Algorithm Selector
      Journal of Artificial Intelligence, Vol. 53, pp. 745-778, August 2015
    • H. Hoos and R. Kaminski and M. Lindauer and T. Schaub
      aspeed: Solver Scheduling via Answer Set Programming
      Theory and Practice of Logic Programming, Vol. 15, pp. 117-142, 2015
    • H. Hoos and M. Lindauer and T. Schaub
      claspfolio 2: Advances in Algorithm Selection for Answer Set Programming
      Theory and Practice of Logic Programming, Vol. 14, pp. 569-585, 2014
    • M. Gebser and R. Kaminski and B. Kaufmann and M. Ostrowskiand T. Schaub and M. Schneider
      Potassco: The {P}otsdam Answer Set Solving Collection
      AI Communications, Vol. 24, No. 2, pp. 107-124, 2011
    • M. M\{"o}ller and M. Schneider and M. Wegner and T. Schaub
      Centurio, a General Game Player: Parallel, Java- and ASP-based
      K\{"u}nstliche Intelligenz, Vol. 25, No. 1, pp. 17-24, 2011
  • Conference Contributions
    • M. Lindauer and M. Feurer and K. Eggensperger and A. Biedenkapp and F. Hutter
      Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters
      {IJCAI} 2019 {DSO} Workshop, August 2019
    • A. Biedenkapp and H. F. Bozkurt and F. Hutter and M. Lindauer
      Towards White-box Benchmarks for Algorithm Control
      {IJCAI} 2019 {DSO} Workshop, August 2019
    • L. Fuks and N. Awad and F. Hutter and M. Lindauer
      An Evolution Strategy with Progressive Episode Lengths for Playing Games
      Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’19), pp. 1234-1240, August 2019
    • M. Lindauer and J. N. van Rijn and L. Kotthoff
      The Algorithm Selection Competitions 2015 and 2017
      Artificial Intelligence, pp. 1-35, December 2018
    • 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
    • K. Eggensperger and M. Lindauer and F. Hutter
      Neural Networks for Predicting Algorithm Runtime Distributions
      Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’18), pp. 1442-1448, July 2018
    • A. Biedenkapp and J. Marben and M. Lindauer and F. Hutter
      CAVE: Configuration Assessment, Visualization and Evaluation
      Proceedings of the International Conference on Learning and Intelligent Optimization (LION'18), June 2018
    • M. Lindauer and F. Hutter
      Warmstarting of Model-based Algorithm Configuration
      Proceedings of the AAAI conference, pp. 1355--1362, February 2018
    • M. Lindauer and J. van Rijn and L. Kotthoff
      Open Algorithm Selection Challenge 2017: Setup and Scenarios
      Proceedings of the Open Algorithm Selection Challenge, PMLR, Vol. 79, pp. 1--7, December 2017, edited by Marius Lindauer and Jan N. van Rijn and Lars Kotthoff
    • M. Lindauer and H. Hoos and F. Hutter and T. Schaub
      AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract)
      Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'17), May 2017
    • M. Wagner and T. Friedrich and M. Lindauer
      Improving local search in a minimum vertex cover solver for classes of networks
      Proceedings of the IEEE Congress on Evolutionary Computation (CEC), March 2017
    • A. Biedenkapp and M. Lindauer and K. Eggensperger and C. Fawcett and H. Hoos and F. Hutter
      Efficient Parameter Importance Analysis via Ablation with Surrogates
      Proceedings of the AAAI conference, pp. 773--779, February 2017
    • M. Lindauer and L. Kotthoff
      What can we learn from algorithm selection data? (Breakout Session Report)
      Dagstuhl Reports, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, Vol. 6, pp. 64-65, February 2017
    • M. Lindauer and F. Hutter
      Pitfalls and Best Practices for Algorithm Configuration (Breakout Session Report)
      Dagstuhl Reports, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, Vol. 6, pp. 70-72, February 2017
    • N. Manthey and M. Lindauer
      SpyBug: Automated Bug Detection in the Configuration Space of SAT Solvers
      Proceedings of the International Conference on Satisfiability Solving (SAT'16), April 2016
    • Rolf-David Bergdoll and Frank Hutter Marius Lindauer
      An Empirical Study of Per-Instance Algorithm Scheduling
      Proceedings of the International Conference on Learning and Intelligent Optimization (LION'16), 2016
    • S. Falkner and M. Lindauer and F. Hutter
      SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers
      Proceedings of the International Conference on Satisfiability Solving (SAT'15), pp. 1-8, August 2015
    • M. Lindauer and H. Hoos and F. Hutter and T. Schaub
      AutoFolio: Algorithm Configuration for Algorithm Selection
      Proceedings of the Twenty-Ninth AAAI Workshops on Artificial Intelligence, January 2015
    • M. Lindauer and H. Hoos and and F. Hutter
      From Sequential Algorithm Selection to Parallel Portfolio Selection
      Proceedings of the International Conference on Learning and Intelligent Optimization (LION'15), January 2015
    • Frank Hutter and Manuel L\'{o}pez-Ib\'{a}\~{n}ez and Chris Fawcett and Marius Lindauer and Holger Hoos and Kevin Leyton-Brown and Thomas St\"utzle
      AClib: a Benchmark Library for Algorithm Configuration
      Proceedings of the Learning and Intelligent OptimizatioN Conference (LION 8), January 2014
    • H. Hoos and B. Kaufmann and T. Schaub and M. Schneider
      Robust Benchmark Set Selection for Boolean Constraint Solvers
      Proceedings of the Seventh International Conference onLearning and Intelligent Optimization (LION'13), Springer-Verlag, Vol. 7997, pp. 138-152, 2013, edited by P. Pardalos and G. Nicosia
    • M. Gebser and H. Jost and R. Kaminski and P. Obermeier and O. Sabuncu and T. Schaub and M. Schneider
      Ricochet Robots: A transverse {ASP} benchmark
      Proceedings of the Twelfth International Conference onLogic Programming and Nonmonotonic Reasoning (LPNMR'13), Springer-Verlag, Vol. 8148, pp. 348-360, 2013, edited by P. Cabalar and T. Son
    • H. Hoos and K. Leyton-Brown and T. Schaub and M. Schneider
      Algorithm Configuration for Portfolio-based Parallel{SAT}-Solving
      Proceedings of the First Workshop on Combining ConstraintSolving with Mining and Learning (CoCoMile'12), 2012, edited by R. Coletta and T. Guns and B. O'Sullivan and A. Passeriniand G. Tack
    • H. Hoos and R. Kaminski and T. Schaub and M. Schneider
      aspeed: {ASP}-based Solver Scheduling
      Technical Communications of the Twenty-eighth InternationalConference on Logic Programming (ICLP'12), Leibniz International Proceedings in Informatics (LIPIcs), Vol. 17, pp. 176-187, 2012, edited by A. Dovier and V. {Santos Costa}
    • B. Silverthorn and Y. Lierler and M. Schneider
      Surviving Solver Sensitivity: An {ASP} Practitioner's Guide
      Technical Communications of the Twenty-eighth InternationalConference on Logic Programming (ICLP'12), Leibniz International Proceedings in Informatics (LIPIcs), Vol. 17, pp. 164-175, 2012, edited by A. Dovier and V. {Santos Costa}
    • M. Schneider and H. Hoos
      Quantifying Homogeneity of Instance Sets for AlgorithmConfiguration
      Proceedings of the Sixth International Conference onLearning and Intelligent Optimization (LION'12), Springer-Verlag, 2012, edited by Y. Hamadi and M. Schoenauer
    • B. Kaufmann and T. Schaub and M. Schneider
      clasp, claspfolio, aspeed: Three Solvers from the AnswerSet Solving Collection {P}otassco
      Proceedings of {SAT} Challenge 2012: Solver and BenchmarkDescriptions, University of Helsinki, pp. 17-19, 2012, edited by A. Balint and A. Belov and D. Diepold and S. Gerber andM. J{\"a}rvisalo and C. Sinz
    • M. Gebser and R. Kaminski and B. Kaufmann and T. Schaub andM. Schneider and S. Ziller
      A Portfolio Solver for Answer Set Programming: PreliminaryReport
      Proceedings of the Eleventh International Conference onLogic Programming and Nonmonotonic Reasoning (LPNMR'11), Springer-Verlag, Vol. 6645, pp. 352-357, 2011, edited by J. Delgrande and W. Faber
  • Technical Report
    • Marius Lindauer and Frank Hutter
      Best Practices for Scientific Research on Neural Architecture Search
      Arxiv/CoRR, September 2019
    • M. Lindauer and K. Eggensperger and M. Feurer and A. Biedenkapp and J. Marben and P. M\"uller and F. Hutter
      BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters
      arXiv:1908.06756 [cs.LG], August 2019
    • M. Lindauer
      Algorithm Selection, Scheduling and Configuration of Boolean Constraint Solvers
      , 2014
Other activities

Research homepage:

Publications:

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