Alumni

Marius Lindauer

Postal address
Institut für Informatik
Albert-Ludwigs-Universität Freiburg
Sekretariat Hutter/Maschinelles Lernen
Georges-Köhler-Allee 074
79110 Freiburg, Germany
Fax
+49 761 203-74217
Office
Building 74, Room 00-015
GoogleScholarMarker

UPDATE

Since Oct. 2019, Marius Lindauer has been appointed as professor for machine learning and the head of AutoML at the University Hannover.

Research Statement

My main research focus lies on the performance tuning of any kind of algorithm (e.g., SAT solvers or machine learning algorithms) using cutting edge techniques from machine learning and optimization. A well-known, but also a tedious, time-consuming and error-prone way to optimize performance (e.g., runtime or prediction loss) is to tune the algorithm’s (hyper-) parameters. To lift the burden on developers and users, I develop methods to automate the process of parameter tuning and algorithm selection for a given problem at hand (e.g., a machine learning dataset, or a set of SAT formulas). To this end, I provide ready-to-use, push-button software that enables users to optimize their software in an easy and efficient way.

Projects

Leading

  • SMAC v3: automatic tuning of parameter configurations on any kind of algorithms (algorithm configurator)
  • CAVE : Configuration Assessment, Visualization and Evaluation
  • PIMP : analysis of parameter importance for parameterized algorithms
  • AutoFolio: state-of-the-art, robust algorithm selection framework
  • SpyBugC: automatic detection of bugs in configuration spaces
  • SpySMAC: an easy-to-use toolkit (primarily designed for SAT solvers) to optimize parameter configurations and analyze the outcome
  • FlexFolio: automatic selection of well-performing configurations for a problem at hand (algorithm selection; successor of claspfolio)
  • AClib: library of algorithm configuration benchmarks
  • ASlib: library of algorithm selection benchmarks
  • piclasp: a toolkit for performance tuning of the parameters of the state-of-the-art ASP solver clasp
  • Parallel Portfolios: automatic construction of parallel portfolio solvers via algorithm configuration
  • claspfolio: a portfolio based automated parameter configurator for the state-of-the-art ASP solver clasp
  • Aspeed: using cutting-edge combinatorial optimization based on answer set programming to determine a well-performing schedule of algorithms (discontinued)
  • xorro: Sampling of answer sets (discontinued)
  • Centurio: general game playing project in Potsdam (discontinued)

Involved

Teaching

  • 2018: Machine Learning for Automated Algorithm Design (Lecture)
  • 2018: Advanced Topics in Automated Algorithm Design: Neural Architecture Search (Seminar)
  • 2018: Machine Learning for Automated Algorithm Design (Lab Course)
  • 2017: Reinforcement Learning (Lecture)
  • 2017: Machine Learning for Automated Algorithm Design (Lecture)
  • 2017: Advanced Deep Learning (Seminar)
  • 2016: Algorithm Configuration: A Hands-on Tutorial (tutorial at AAAI'16)
  • 2015: Machine Learning and Optimization for Algorithm Design (lecture)
  • 2015: Automated Parameter tuning (practical training)
  • 2014: AI for Automated Algorithm Design (seminar)
  • 2014: Automated Parameter Tuning and Algorithm Configuration (seminar)
  • 2013: Stochastic Optimization (lecture)
  • 2012: AI for Games (part of lecture “Knowledge Representation and Reasoning”)
  • 2011: Applied Logic (teaching assistant)
  • 2008+2010+2011: practical class about Answer Set Programming (winter term)
  • 2009: AI for Games (seminar)
  • 2009-2013 : practical class about General Game Playing (each summer term)
  • 2006-2009: tutorial class about Theoretical Informatics

Short CV

  • since 2017: Junior research group lead at the University of Freiburg
  • 2014-2017: Postdoctoral research fellow at the University of Freiburg
  • 2015: Phd in computer science at the University of Potsdam
  • 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

Links

Publications

2021

Benjamins, Carolin; Eimer, Theresa; Schubert, Frederik; Biedenkapp, André; Rosenhan, Bodo; Hutter, Frank; Lindauer, Marius

CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning Inproceedings

In: arXiv:2110.02102, 2021.

Lindauer, Marius; Eggensperger, Katharina; Feurer, Matthias; Biedenkapp, André; Deng, Difan; Benjamins, Carolin; Sass, René; Hutter, Frank

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization Journal Article

In: arXiv:2109.09831, 2021.

Eggensperger, Katharina; Müller, Philipp; Mallik, Neeratyoy; Feurer, Matthias; Sass, René; Klein, Aaron; Awad, Noor; Lindauer, Marius; Hutter, Frank

HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO Journal Article

In: arXiv:2109.06716, 2021.

Feurer, Matthias; Eggensperger, Katharina; Falkner, Stefan; Lindauer, Marius; Hutter, Frank

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning Journal Article

In: arXiv:2007.04074, 2021.

Eimer, Theresa; Biedenkapp, André; Reimer, Maximilian; Adriaensen, Steven; Hutter, Frank; Lindauer, Marius

DACBench: A Benchmark Library for Dynamic Algorithm Configuration Inproceedings

In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI'21), ijcai.org, 2021.

Speck, David; Biedenkapp, André; Hutter, Frank; Mattmüller, Robert; Lindauer, Marius

Learning Heuristic Selection with Dynamic Algorithm Configuration Inproceedings

In: Proceedings of the 31st International Conference on Automated Planning and Scheduling (ICAPS'21), 2021.

Izquierdo, Sergio; Guerrero-Viu, Julia; Hauns, Sven; Miotto, Guilherme; Schrodi, Simon; Biedenkapp, André; Elsken, Thomas; Deng, Difan; Lindauer, Marius; Hutter, Frank

Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization Inproceedings

In: Workshop on Automated Machine Learning (AutoML@ICML'21), 2021.

Eimer, Theresa; Biedenkapp, André; Hutter, Frank; Lindauer, Marius

Self-Paced Context Evaluations for Contextual Reinforcement Learning Inproceedings

In: Proceedings of the 38th International Conference on Machine Learning (ICML 2021), 2021.

Biedenkapp, André; Rajan, Raghu; Hutter, Frank; Lindauer, Marius

TempoRL: Learning When to Act Inproceedings

In: Proceedings of the 38th International Conference on Machine Learning (ICML 2021), 2021.

Zimmer, Lucas; Lindauer, Marius; Hutter, Frank

Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL Journal Article

In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1, 2021.

Souza, Artur; Nardi, Luigi; Oliveira, Leonardo; Olukotun, Kunle; Lindauer, Marius; Hutter, Frank

Bayesian Optimization with a Prior for the Optimum Inproceedings

In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2021.

2020

Awad, Noor; Shala, Gresa; Deng, Difan; Mallik, Neeratyoy; Feurer, Matthias; Eggensperger, Katharina; Biedenkapp, André; Vermetten, Diederick; Wang, Hao; Doerr, Carola; Lindauer, Marius; Hutter, Frank

Squirrel: A Switching Hyperparameter Optimizer Description of the entry by AutoML.org & IOHprofiler to the NeurIPS 2020 BBO challenge Journal Article

In: arXiv:2012.08180 [cs.LG], 2020, (Optimizer description for the NeurIPS 2020 BBO competition. Squirrel won the competition´s warm-starting friendly leaderboard.).

Lindauer, Marius; Hutter, Frank

Best Practices for Scientific Research on Neural Architecture Search Journal Article

In: Journal of Machine Learning Research, 21 (243), pp. 1-18, 2020.

Speck, David; Biedenkapp, André; Hutter, Frank; Mattmüller, Robert; Lindauer, Marius

Learning Heuristic Selection with Dynamic Algorithm Configuration Inproceedings

In: Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL@ICAPS'20), 2020.

Liu, Zhengying; Pavao, Adrien; Xu, Zhen; Escalera, Sergio; Ferreira, Fabio; Guyon, Isabelle; Hong, Sirui; Hutter, Frank; Ji, Rongrong; Junior, Julio C S Jacques; Li, Ge; Lindauer, Marius; Luo, Zhipeng; Madadi, Meysam; Nierhoff, Thomas; Niu, Kangning; Pan, Chunguang; Stoll, Danny; Treguer, Sebastien; Wang, Jin; Wang, Peng; Wu, Chenglin; Xiong, Youcheng; Zela, Arber; Zhang, Yang

Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019 Journal Article

In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 43 (9), pp. 3108-3125, 2020.

Souza, Artur; Nardi, Luigi; Oliveira, Leonardo B; Olukotun, Kunle; Lindauer, Marius; Hutter, Frank

Prior-guided Bayesian Optimization Journal Article

In: NeurIPS 4th Workshop on Meta-Learning, 2020.

Eggensperger, Katharina; Haase, Kai; Müller, Philipp; Lindauer, Marius; Hutter, Frank

Neural Model-based Optimization with Right-Censored Observations Journal Article

In: arXiv:2009:13828 [cs.AI], 2020.

Shala, Gresa; Biedenkapp, André; Awad, Noor; Adriaensen, Steven; Lindauer, Marius; Hutter, Frank

Learning Step-Size Adaptation in CMA-ES Inproceedings

In: Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN'20), 2020.

Biedenkapp, André; Rajan, Raghu; Hutter, Frank; Lindauer, Marius

Towards TempoRL: Learning When to Act Inproceedings

In: Workshop on Inductive Biases, Invariances and Generalization in RL (BIG@ICML'20), 2020.

Eimer, Theresa; Biedenkapp, André; Hutter, Frank; Lindauer, Marius

Towards Self-Paced Context Evaluations for Contextual Reinforcement Learning Inproceedings

In: Workshop on Inductive Biases, Invariances and Generalization in RL (BIG@ICML'20), 2020.

Zimmer, Lucas; Lindauer, Marius; Hutter, Frank

Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL Journal Article

In: arXiv:2006.13799 [cs.LG], 2020.

Biedenkapp, André; Bozkurt, Furkan H; Eimer, Theresa; Hutter, Frank; Lindauer, Marius

Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework Inproceedings

In: Proceedings of the Twenty-fourth European Conference on Artificial Intelligence (ECAI'20), 2020.

2019

Fuks, L; Awad, N; Hutter, F; Lindauer, M

An Evolution Strategy with Progressive Episode Lengths for Playing Games Inproceedings

In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’19), 2019.

Lindauer, Marius; Feurer, Matthias; Eggensperger, Katharina; Biedenkapp, André; Hutter, Frank

Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters Inproceedings

In: IJCAI 2019 DSO Workshop, 2019.

Lindauer, Marius; Eggensperger, Katharina; Feurer, Matthias; Biedenkapp, André; Marben, Joshua; Müller, Philipp; Hutter, Frank

BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters Journal Article

In: arXiv:1908.06756 [cs.LG], 2019.

Biedenkapp, André; Bozkurt, Furkan H; Hutter, Frank; Lindauer, Marius

Towards White-box Benchmarks for Algorithm Control Inproceedings

In: IJCAI 2019 DSO Workshop, 2019.

Mendoza, Hector; Klein, Aaron; Feurer, Matthias; Springenberg, Jost Tobias; Urban, Matthias; Burkart, Michael; Dippel, Max; Lindauer, Marius; Hutter, Frank

Towards Automatically-Tuned Deep Neural Networks Incollection

In: Hutter, Frank; Kotthoff, Lars; Vanschoren, Joaquin (Ed.): AutoML: Methods, Sytems, Challenges, pp. 135–149, Springer, 2019.

Eggensperger, Katharina; Lindauer, Marius; Hutter, Frank

Pitfalls and Best Practices in Algorithm Configuration Journal Article

In: Journal of Artificial Intelligence Research (JAIR), 64 , pp. 861–893, 2019.

2018

Lindauer, M; van Rijn, J N; Kotthoff, L

The Algorithm Selection Competitions 2015 and 2017 Journal Article

In: Artificial Intelligence, pp. 1-35, 2018.

Eggensperger, Katharina; Lindauer, Marius; Hutter, Frank

Neural Networks for Predicting Algorithm Runtime Distributions Inproceedings

In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’18), pp. 1442-1448, 2018.

Feurer, Matthias; Eggensperger, Katharina; Falkner, Stefan; Lindauer, Marius; Hutter, Frank

Practical Automated Machine Learning for the AutoML Challenge 2018 Inproceedings

In: ICML 2018 AutoML Workshop, 2018.

Biedenkapp, André; Marben, Joshua; Lindauer, Marius; Hutter, Frank

CAVE: Configuration Assessment, Visualization and Evaluation Inproceedings

In: Proceedings of the International Conference on Learning and Intelligent Optimization (LION'18), 2018.

Lindauer, M; Hutter, F

Warmstarting of Model-based Algorithm Configuration Inproceedings

In: Proceedings of the AAAI conference, pp. 1355–1362, 2018.

Eggensperger, Katharina; Lindauer, Marius; Hoos, Holger H; Hutter, Frank; Leyton-Brown, Kevin

Efficient Benchmarking of Algorithm Configurators via Model-Based Surrogates Journal Article

In: Machine Learning, 107 , pp. 15-41, 2018.

2017

Lindauer, Marius; van Rijn, Jan N; Kotthoff, Lars

Open Algorithm Selection Challenge 2017: Setup and Scenarios Inproceedings

In: Lindauer, Marius; van Rijn, Jan N; Kotthoff, Lars (Ed.): Proceedings of the Open Algorithm Selection Challenge, pp. 1–7, PMLR, Brussels, Belgium, 2017.

Lindauer, M; Hoos, H; Hutter, F; Schaub, T

AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract) Inproceedings

In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'17), 2017.

Wagner, M; Friedrich, T; Lindauer, M

Improving local search in a minimum vertex cover solver for classes of networks Inproceedings

In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 2017.

Wagner, M; Lindauer, M; Misir, M; Nallaperuma, S; Hutter, F

A case study of algorithm selection for the traveling thief problem Journal Article

In: Journal of Heuristics, pp. 1-26, 2017.

Lindauer, M; Hoos, H; Leyton-Brown, K; Schaub, T

Automatic Construction of Parallel Portfolios via Algorithm Configuration Journal Article

In: Artificial Intelligence Journal (AIJ), 244 , pp. 272-290, 2017.

Hutter, F; Lindauer, M; Balint, A; Bayless, S; Hoos, H; Leyton-Brown, K

The Configurable SAT Solver Challenge (CSSC) Journal Article

In: Artificial Intelligence Journal (AIJ), 243 , pp. 1-25, 2017.

Lindauer, M; Hutter, F

Pitfalls and Best Practices for Algorithm Configuration (Breakout Session Report) Journal Article

In: Dagstuhl Reports, 6 , pp. 70-72, 2017.

Lindauer, M; Kotthoff, L

What can we learn from algorithm selection data? (Breakout Session Report) Journal Article

In: Dagstuhl Reports, 6 , pp. 64-65, 2017.

Biedenkapp, André; Lindauer, Marius; Eggensperger, Katharina; Fawcett, Chris; Hoos, Holger H; Hutter, Frank

Efficient Parameter Importance Analysis via Ablation with Surrogates Inproceedings

In: Proceedings of the Thirty-First Conference on Artificial Intelligence (AAAI'17), pp. 773–779, 2017.

Lindauer, M; Hoos, H; Hutter, F; Leyton-Brown, K

Selection and Configuration of Parallel Portfolios Incollection

In: Hamadi, Y; Sais, L (Ed.): Handbook of Parallel Constraint Reasoning, Springer, 2017.

2016

Bischl, B; Kerschke, P; Kotthoff, L; Lindauer, M; Malitsky, Y; Frechétte, A; Hoos, H; Hutter, F; Leyton-Brown, K; Tierney, K; Vanschoren, J

ASlib: A Benchmark Library for Algorithm Selection Journal Article

In: Artificial Intelligence Journal (AIJ), 237 , pp. 41-58, 2016.

Manthey, N; Lindauer, M

SpyBug: Automated Bug Detection in the Configuration Space of SAT Solvers Inproceedings

In: Proceedings of the International Conference on Satisfiability Solving (SAT'16), 2016.

2015

Lindauer, M; Hoos, H; Hutter, F; Schaub, T

AutoFolio: An automatically configured Algorithm Selector Journal Article

In: Journal of Artificial Intelligence, 53 , pp. 745-778, 2015.

Falkner, S; Lindauer, M; Hutter, F

SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers Inproceedings

In: Proceedings of the International Conference on Satisfiability Solving (SAT'15), pp. 1-8, 2015.

Lindauer, M; Hoos, H; Hutter, F; Schaub, T

AutoFolio: Algorithm Configuration for Algorithm Selection Inproceedings

In: Proceedings of the Twenty-Ninth AAAI Workshops on Artificial Intelligence, 2015.

Hoos, H; Kaminski, R; Lindauer, M; Schaub, T

aspeed: Solver Scheduling via Answer Set Programming Journal Article

In: Theory and Practice of Logic Programming, 15 , pp. 117-142, 2015.

Lindauer, M; Hoos, H; F,; Hutter,

From Sequential Algorithm Selection to Parallel Portfolio Selection Inproceedings

In: Proceedings of the International Conference on Learning and Intelligent Optimization (LION'15), 2015.

2014

Hoos, H; Lindauer, M; Schaub, T

claspfolio 2: Advances in Algorithm Selection for Answer Set Programming Journal Article

In: Theory and Practice of Logic Programming, 14 , pp. 569-585, 2014.

Lindauer, M

Algorithm Selection, Scheduling and Configuration of Boolean Constraint Solvers PhD Thesis

University of Potsdam, 2014, (Preliminary Version).

Hutter, Frank; López-Ibáñez, Manuel; Fawcett, Chris; Lindauer, Marius; Hoos, Holger; Leyton-Brown, Kevin; Stützle, Thomas

AClib: a Benchmark Library for Algorithm Configuration Inproceedings

In: Proceedings of the Learning and Intelligent OptimizatioN Conference (LION 8), 2014.