Prof. Dr. rer. nat. Marius Lindauer
Leibniz Universität Hannover
Institut für Künstliche Intelligenz
phone: +49 511 762 5301

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)
Show recent publications only
  • Conference Contributions
    • Carl Hvarfner, Danny Stoll, Artur Souza, Marius Lindauer, Frank Hutter, Luigi Nardi
      piBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
      10th International Conference on Learning Representations, ICLR'22, OpenReview, pp. 1-30, April 2022
    • André Biedenkapp, David Speck, Silvan Sievers, Frank Hutter, Marius Lindauer, Jendrik Seipp
      Learning Domain-Independent Policies for Open List Selection
      Proceedings of the 3rd ICAPS workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL), pp. 1-9, 2022
    • Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor Awad, Marius Lindauer, Frank Hutter
      HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
      Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track), December 2021
    • 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
    • Theresa Eimer, Carolin Benjamins, Marius Lindauer
      Hyperparameters in Contextual RL are Highly Situational
      NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning, December 2021
    • Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
      CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
      NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning, 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
    • 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 Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer
      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
      Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML'21, July 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
    • Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
      Towards Explaining Hyperparameter Optimization via Partial Dependence Plots
      Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML'21, July 2021
    • Frederik Schubert, Theresa Eimer, Bodo Rosenhahn, Marius Lindauer
      Towards Automatic Risk Adaption in Distributional Reinforcement Learning
      Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 38th International Conference on Machine Learning (ICML), July 2021
    • 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, 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
    • 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, Raghu Rajan, Frank Hutter, Marius Lindauer
      Towards TempoRL: Learning When to Act
      Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML'20), July 2020
    • David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer
      Learning Heuristic Selection with Dynamic Algorithm Configuration
      Proceedings of international workshop on Bridging the Gap Between AI Planning and Reinforcement Learning at ICAPS, June 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. 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. 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
  • Journals
    • Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, Sebastian Döhler, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
      Contextualize Me - The Case for Context in Reinforcement Learning
      Transactions on Machine Learning Research, June 2023
    • Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer
      POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning
      Transactions on Machine Learning Research, April 2023
    • Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter
      Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
      Journal of Machine Learning Research (JMLR), Vol. 23, No. 261, p. 1−61, October 2022
    • 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
    • Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer
      Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
      Journal of Artificial Intelligence Research (JAIR), 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
    • Zhengying Liu, Adrien Pavao, Zhen Xu, Sergio Escalera, Fabio Ferreira, Isabelle Gyon, Sirui Hong, Frank Hutter, Rongrong Ji, Julio Jacques Junior, Ge Li, Marius Lindauer, Zhipeng Luo, Meysam Madadi, Thomas Nierhoff, Kangning Niu, Chunguang Pan, Danny Stoll, Sebastien Treguer, Wang Jin, Peng Wang, Chenglin Wu, Xiong Youcheng, Arber Zela, Yang Zhang
      Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
      IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE, Vol. 43, No. 9, pp. 3108 - 3125, 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
    • M. Lindauer and J. N. van Rijn and L. Kotthoff
      The Algorithm Selection Competitions 2015 and 2017
      Artificial Intelligence, pp. 86--100, December 2019
    • 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
  • 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
    • 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
    • Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor Awad, Theresa Eimer, Marius Lindauer, Frank Hutter
      Automated Dynamic Algorithm Configuration
      ArXiv, May 2022
    • Lukas Stürenburg, Berend Denkena, Marius Lindauer, Marcel Wichmann
      Maschinelles Lernen in der Prozessplanung
      VDI-Z, VDI Fachmedien GmbH, October 2021
    • Frederik Schubert, Theresa Eimer, Bodo Rosenhahn, Marius Lindauer
      Automatic Risk Adaptation in Distributional Reinforcement Learning
      Arxiv Preprint, June 2021
    • Noor Awad, Gresa Shala, Difan Deng, Neeratyoy Mallik, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Diederick Vermetten, Hao Wang, Doerr Carola, Marius Lindauer, Frank Hutter
      Squirrel: A Switching Hyperparameter Optimizer
      arxiv, December 2020
    • Katharina Eggensperger, Kai Haase, Philipp Müller, Marius Lindauer, Frank Hutter
      Neural Model-based Optimization with Right-Censored Observations
      CoRR, ArXiv, September 2020
    • 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

Important Links:

Publications:

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

Memberships: