InstituteStaff
Daphne Theodorakopoulos

Daphne Theodorakopoulos, M.Sc.

Daphne Theodorakopoulos, M.Sc.
Address
Appelstr. 9A
30167 Hannover
Daphne Theodorakopoulos, M.Sc.
Address
Appelstr. 9A
30167 Hannover

I am a PhD candidate at the AutoML Group. My research topic here is Green AutoML. I am also a researcher at the DFKI Marine Perception in Oldenburg. Our focus is AI for marine perception but also other environmental and sustainability topics. I aim to find a good intersection between the two departments by using Green AutoML to tackle environmental problems. 

Research Interests

  • Green AutoML
  • AI for Sustainability
  • Knowledge Graphs

Curriculum Vitae

  • Working Experience

    since 2022
    Researcher
    , DFKI Marine Perception

    2020 - 2022
    Teaching Assistant
    , University of Twente (NL)

    2019
    Internship
    Big Data & Advanced Analytics, Daimler AG

    2016 - 2018
    Teaching Assistant
    , University of Hamburg

  • Education

    2022 - Present
    PhD Candidate, Leibniz University Hannover

    2019-2022
    Master of Science, Interaction Technology, University of Twente (NL)

    2015-2019
    Bachelor of Science, Wirtschaftsinformatik, University of Hamburg

Publications

  • Neeratyoy Mallik, Carl Hvarfner, Danny Stoll, Maciej Janowski, Eddie Bergman, Marius Lindauer, Luigi Nardi, Frank Hutter (2022): PriorBand: HyperBand + Human Expert Knowledge2022 NeurIPS Workshop on Meta Learning (MetaLearn)
  • Carolin Benjamins, Anja Jankovic, Elena Raponi, Koen van der Blom, Marius Lindauer, Carola Doerr (2022): Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis6th Workshop on Meta-Learning at NeurIPS 2022, New Orleans
  • René Sass, Eddie Bergman, André Biedenkapp, Frank Hutter, Marius Lindauer (2022): DeepCAVE: An Interactive Analysis Tool for Automated Machine LearningICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML)
    arXiv: 2206.03493
  • Aditya Mohan, Tim Ruhkopf, Marius Lindauer (2022): Towards Meta-learned Algorithm Selection using Implicit Fidelity InformationICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML)
    arXiv: 2206.03130
  • Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter (2022): Automated Dynamic Algorithm ConfigurationComputing Research Repository (CoRR) (2022)
    DOI: https://doi.org/10.48550/arXiv.2205.13881
    arXiv: 2205.13881
  • Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer (2022): POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement LearningCoRR
    arXiv: arXiv:2205.11357
  • Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer (2022): Efficient Automated Deep Learning for Time Series ForecastingProceedings of the European Conference on Machine Learning (ECML)
    arXiv: 2205.05511
  • Carl Hvarfner, Danny Stoll, Artur Souza, Marius Lindauer, Frank Hutter, Luigi Nardi (2022): piBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization10th International Conference on Learning Representations, ICLR'22
    arXiv: https://openreview.net/forum?id=MMAeCXIa89
  • Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer (2022): Contextualize Me - The Case for Context in Reinforcement LearningArXiv Preprint
    arXiv: https://arxiv.org/abs/2202.04500
  • Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter (2022): SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter OptimizationJournal of Machine Learning Research (JMLR) -- MLOSS, Vol. 23, No. 54, pp. 1-9 More Info
  • 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 (2022): Automated Reinforcement Learning (AutoRL): A Survey and Open ProblemsJournal of Artificial Intelligence Research (JAIR)
    arXiv: 2201.03916
  • Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka (2021): Well-tuned Simple Nets Excel on Tabular DatasetsAdvances in Neural Information Processing Systems (NeurIPS 2021)
    arXiv: 2106.11189
  • Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl (2021): Explaining Hyperparameter Optimization via Partial Dependence PlotsProceedings of the international conference on Neural Information Processing Systems (NeurIPS) | File |
    arXiv: 2111.04820
  • Theresa Eimer, Carolin Benjamins, Marius Lindauer (2021): Hyperparameters in Contextual RL are Highly SituationalInternational Workshop on Ecological Theory of RL (at NeurIPS) | File |
  • Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka (2021): Well-tuned Simple Nets Excel on Tabular DatasetsNeurIPS 2021
    DOI: https://doi.org/10.48550/arXiv.2106.11189
    arXiv: 2106.11189
  • Difan Deng, Marius Lindauer (2021): Searching in the Forest for Local Bayesian OptimizationECMLPKDD Workshop on Meta-Knowledge Transfe
    arXiv: 2111.05834
  • Lukas Stürenburg, Berend Denkena, Marius Lindauer, Marcel Wichmann (2021): Maschinelles Lernen in der Prozessplanung
  • Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer (2021): CARL: A Benchmark for Contextual and Adaptive Reinforcement LearningWorkshop on Ecological Theory of Reinforcement Learning, NeurIPS 2021
    arXiv: 2110.02102
  • Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor Awad, Marius Lindauer, Frank Hutter (2021): HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOProceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track)
    arXiv: 2109.06716
  • Artur Souza, Luigi Nardi, Leonardo B Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter (2021): Bayesian Optimization with a Prior for the OptimumProceedings of the European conference on machine learning (ECML)
    arXiv: 2006.14608
  • Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl (2022): Developing Open Source Educational Resources for Machine Learning and Data ScienceTeaching Machine Learning Workshop at ECML 2022
    arXiv: 2107.14330
  • Bernd Bischl, Martin Binder, Michel Lang, Tobias Pielok, Jakob Richter, Stefan Coors, Janek Thomas, Theresa Ullmann, Marc Becker, Anne-Laure Boulesteix, Difan Deng, Marius Lindauer (2021): Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open ChallengesWIREs Data Mining and Knowledge Discovery
    DOI: 10.1002/widm.1484
    arXiv: 2107.05847
  • Frederik Schubert, Theresa Eimer, Bodo Rosenhahn, Marius Lindauer (2021): Automatic Risk Adaptation in Distributional Reinforcement Learning
    arXiv: 2106.06317
  • Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer (2021): Self-Paced Context Evaluation for Contextual Reinforcement LearningProceedings of the international conference on machine learning (ICML)
    arXiv: 2106.05110
  • André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer (2021): TempoRL: Learning When to ActProceedings of the international conference on machine learning (ICML) 2021
    arXiv: 2106.05262
  • Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl (2021): Towards Explaining Hyperparameter Optimization via Partial Dependence Plots8th ICML Workshop on Automated Machine Learning (AutoML) | File |
  • David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer (2021): Learning Heuristic Selection with Dynamic Algorithm ConfigurationInternational Conference on Automated Planning and Scheduling (ICAPS) 2021 | File |
    arXiv: 2006.08246
  • Sergio Izquierdo, Julia Guerrero-Viu, Sven Hauns, Guilherme Miotto, Simon Schrodi, André Biedenkapp, Thomas Elsken, Difan Deng, Marius Lindauer, Frank Hutter (2021): Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter OptimizationICML 2021 Workshop AutoML
    arXiv: 2105.01015
  • Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer (2021): DACBench: A Benchmark Library for Dynamic Algorithm ConfigurationProceedings of the international joint conference on AI (IJCAI) 2021
    arXiv: 2105.08541
  • Zhengying Liu, Adrien Pavao, Zhen Xu, Sergio Escalera, Fabio Ferreira, Isabelle Guyon, Sirui Hong, Frank Hutter, Rongrong Ji, Julio CS Jacques Junior, Ge Li, Marius Lindauer, Zhipeng Luo, Meysam Madadi, Thomas Nierhoff, Kangning Niu, Chunguang Pan, Danny Stoll, Sebastien Treguer, Jin Wang, Peng Wang, Chenglin Wu, Youcheng Xiong, Arbër Zela, Yang Zhang (2021): Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019 | File |
  • Lucas Zimmer, Marius Lindauer, Frank Hutter (2021): Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL.IEEE Transactions on Pattern Analysis and Machine Intelligence
    arXiv: 2006.13799
  • Noor Awad, Gresa Shala, Difan Deng, Neeratyoy Mallik, Matthias Feurer, Katharina Eggensperger, Andre' Biedenkapp, Diederick Vermetten, Hao Wang, Carola Doerr, Marius Lindauer, Frank Hutter (2020): Squirrel: A Switching Hyperparameter Optimize
    arXiv: 2012.08180
  • Berend Denkena, Marc-André Dittrich, Marius Lindauer, Julia Mainka, Lukas Stürenburg (2020): Using AutoML to Optimize Shape Error Prediction in Milling ProcessesProceedings of the Machining Innovations Conference (MIC) | File |
  • Marius Lindauer, Frank Hutter (2019): Best Practices for Scientific Research on Neural Architecture SearchComputing Research Repository (CoRR) (2019)
    DOI: https://doi.org/10.48550/arXiv.1908.06756
    arXiv: 1908.06756
  • Katharina Eggensperger, Kai Haase, Philipp Müller, Marius Lindauer, Frank Hutter (2020): Neural Model-based Optimization with Right-Censored Observations
    arXiv: 2009.13828
  • Artur L. F. Souza, Luigi Nardi, Leonardo B. Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter (2020): Prior-guided Bayesian OptimizationComputing Research Repository (CoRR) (2020) | File |
  • Gresa Shala, André Biedenkapp, Noor Awad, Steven Adriaensen, Marius Lindauer, Frank Hutter (2020): Learning Step-Size Adaptation in CMA-ESProceedings of international PPSN conference 2020 | File |
  • Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer (2020): Towards Self-Paced Context Evaluation for Contextual Reinforcement LearningInternational Conference on Machine Learning (ICML) 2020 | File |
  • André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer (2020): Towards TempoRL: Learning When to ActInternational Conference on Machine Learning (ICML) 2020 | File |
  • Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter (2022): Auto-Sklearn 2.0: The Next GenerationJournal of Machine Learning Research (JMLR), 23(261):1−61 More Info
    arXiv: 2007.04074
  • André Biedenkapp, H Furkan Bozkurt, Theresa Eimer, Frank Hutter, Marius Lindauer (2020): Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic FrameworkEuropean Conference on AI (ECAI) 2020 | File |
  • Marius Lindauer, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Frank Hutter (2019): Towards Assessing the Impact of Bayesian Optimization’s Own HyperparametersComputing Research Repository (CoRR) (2019)
    DOI: https://doi.org/10.48550/arXiv.1908.06674
    arXiv: 1908.06674
  • Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Joshua Marben, Philipp Müller, Frank Hutter (2019): BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of HyperparametersComputing Research Repository (CoRR) (2019)
    DOI: https://doi.org/10.48550/arXiv.1908.06756
    arXiv: 1908.06756
  • Marius Lindauer, Jan N. van Rijn, Lars Kotthoff (2019): The Algorithm Selection Competitions 2015 and 2017Artif. Intell. (2019)
    arXiv: 1805.01214
  • Hector Mendoza, Aaron Klein, Matthias Feuer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart, Maximilian Dippel, Marius Lindauer, Frank Hutter (2019): Towards Automatically-Tuned Deep Neural NetworksAutomated Machine Learning
    DOI: https://doi.org/10.1007/978-3-030-05318-5
    ISBN: 978-3-030-05317-8
    ISSN: 2520-131X
  • Andre Biedenkapp, Joshua Marben, Marius Lindauer, Frank Hutter (2018): CAVE: Configuration Assessment, Visualization and EvaluationLearning and Intelligent Optimization (LION) 2018 | File |
  • Katharina Eggensperger, Marius Lindauer, Frank Hutter (2018): Neural Networks for Predicting Algorithm Runtime DistributionsInternational Joint Conference on Artificial Intelligence (IJCAI) 2018
    DOI: https://doi.org/10.48550/arXiv.1709.07615
    arXiv: 1709.07615
  • Marius Lindauer, Frank Hutter (2017): Warmstarting of Model-based Algorithm ConfigurationComputing Research Repository (CoRR) (2017)
    DOI: https://doi.org/10.48550/arXiv.1709.04636
    arXiv: 1709.04636
  • Marius Lindauer, Jan N. van Rijn, Lars Kotthoff (2017): Open Algorithm Selection Challenge 2017 Setup and ScenariosOpen Algorithm Selection Challenge 2017 Setup and Scenarios (OASC) 2017 | File |
  • Markus Wagner, Tobias Friedrich, Marius Lindauer (2017): Improving local search in a minimum vertex cover solver for classes of networksIEEE Congress on Evolutionary Computation (CEC) 2017
    DOI: 10.1109/CEC.2017.7969507
    ISBN: 978-1-5090-4602-7
  • Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown (2017): Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based SurrogatesComputing Research Repository (CoRR) (2017)
    DOI: https://doi.org/10.48550/arXiv.1703.10342
    arXiv: 1703.10342
  • Katharina Eggensperger, Marius Lindauer, Frank Hutter (2017): Pitfalls and Best Practices in Algorithm ConfigurationComputing Research Repository (CoRR) (2017)
    DOI: https://doi.org/10.48550/arXiv.1705.06058
    arXiv: 1705.06058
  • Marius Lindauer, Holger H. Hoos, Kevin Leyton-Brown, Torsten Schaub (2017): Automatic Construction of Parallel Portfolios via Algorithm ConfigurationArtif. Intell. (2017)
    DOI: https://doi.org/10.1016/j.artint.2016.05.004
  • Andre Biedenkapp, Marius Lindauer, Katharina Eggensperger, Frank Hutter, Chris Fawcett, Holger H. Hoos (2017): Efficient Parameter Importance Analysis via Ablation with SurrogatesAssociation for the Advancement of Artificial Intelligence (AAAI) 2017
    DOI: https://doi.org/10.1609/aaai.v31i1.10657
  • Marius Lindauer, Rolf-David Bergdoll, Frank Hutter (2016): An Empirical Study of Per-Instance Algorithm SchedulingLearning and Intelligent Optimization (LION) 2016
    DOI: https://doi.org/10.1007/978-3-319-50349-3_20
    ISBN: 978-3-319-50348-6
  • Markus Wagner, Marius Lindauer, Mustafa Misir, Samadhi Nallaperuma, Frank Hutter (2016): A case study of algorithm selection for the traveling thief problemComputing Research Repository (CoRR) (2016)
    arXiv: 1609.00462
  • Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger H. Hoos, Kevin Leyton-Brown (2016): The Configurable SAT Solver Challenge (CSSC)Computing Research Repository (CoRR) (2015)
    DOI: https://doi.org/10.48550/arXiv.1505.01221
    arXiv: 1505.01221
  • Norbert Manthey, Marius Lindauer (2016): SpyBug: Automated Bug Detection in the Configuration Space of SAT SolversSAT 2016
    DOI: 10.1007/978-3-319-40970-2_36
  • Stefan Falkner, Marius Lindauer, Frank Hutter (2015): SpySMAC: Automated Configuration and Performance Analysis of SAT SolversSAT 2015 | File |
  • Marius Lindauer, Holger H. Hoos, Frank Hutter, Torsten Schaub (2015): AutoFolio: An Automatically Configured Algorithm SelectorJournal of Artificial Intelligence Research (2015)
    DOI: https://doi.org/10.1613/jair.4726
  • Marius Lindauer, Holger H. Hoos, Frank Hutter, Torsten Schaub: (2015): AutoFolio: Algorithm Configuration for Algorithm SelectionAssociation for the Advancement of Artificial Intelligence (AAAI) Workshop: Algorithm Configuration 2015 | File |
  • Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren (2015): ASlib: A Benchmark Library for Algorithm SelectionComputing Research Repository (CoRR) (2015)
    DOI: https://doi.org/10.48550/arXiv.1506.02465
    arXiv: 1506.02465
  • Marius Lindauer, Holger H. Hoos, Frank Hutter (2015): From Sequential Algorithm Selection to Parallel Portfolio SelectionLearning and Intelligent Optimization (LION) 2015
    DOI: 10.1007/978-3-319-19084-6_1
  • Marius Lindauer (2014): Algorithm Selection, Scheduling and Configuration of Boolean Constraint Solvers | File |
  • Holger Hoos, Marius Lindauer, Torsten Schaub (2014): claspfolio 2: Advances in Algorithm Selection for Answer Set ProgrammingTheory and Practice of Logic Programming
    DOI: 10.1017/S1471068414000210
    arXiv: 1405.1520
  • Frank Hutter, Manuel López-Ibáñez, Chris Fawcett, Marius Lindauer, Holger Hoos, Kevin Leyton-Brown and Thomas Stützle (2014): AClib: a Benchmark Library for Algorithm ConfigurationLearning and Intelligent Optimization (LION 8)
    DOI: https://doi.org/10.1007/978-3-319-09584-4
    ISBN: 978-3-319-09583-7
    ISSN: 0302-9743
  • Holger H. Hoos, Benjamin Kaufmann, Torsten Schaub, Marius Schneider (2013): Robust Benchmark Set Selection for Boolean Constraint SolversLearning and Intelligent Optimization (LION) 2013
    DOI: https://doi.org/10.1007/978-3-642-44973-4_16
    ISBN: 978-3-642-44972-7
  • Bryan Silverthorn, Yuliya Lierler, Marius Schneider (2012): Surviving Solver Sensitivity: An ASP Practitioner’s GuideInternational Conference on Logic Programming (ICLP (Technical Communications)) 2012
    DOI: 10.4230/LIPIcs.ICLP.2012.164
    ISBN: 978-3-939897-43-9
    ISSN: 1868-8969
  • Holger H. Hoos, Roland Kaminski, Torsten Schaub, Marius Schneider (2012): aspeed: ASP-based Solver SchedulingInternational Conference on Logic Programming (ICLP (Technical Communications)) 2012 | File |
    ISBN: 978-3-939897-43-9
    ISSN: 1868-8969
  • Marius Schneider, Holger H. Hoos (2012): Quantifying Homogeneity of Instance Sets for Algorithm ConfigurationLearning and Intelligent Optimization (LION) 2012
    DOI: https://doi.org/10.1007/978-3-642-34413-8_14
    ISBN: 978-3-642-34412-1
  • Holger Hoos, Kevin Leyton-Brown, Torsten Schaub, Marius Schneider (2012): Algorithm Configuration for Portfolio-based Parallel SAT-SolvingProceedings of the ECAI-12 Workshop on Combining Constraint Solving with MIning and Learning | File |
  • Martin Gebser, Roland Kaminski, Benjamin Kaufmann, Torsten Schaub, Marius Thomas Schneider, Stefan Ziller (2011): A Portfolio Solver for Answer Set Programming: Preliminary ReportLogic Programming and Non-Monotonic Reasoning (LPNMR) 2011
    DOI: https://doi.org/10.1007/978-3-642-20895-9_40
    ISBN: 978-3-642-20894-2
  • Martin Gebser, Benjamin Kaufmann, Roland Kaminski, Max Ostrowski, Torsten Schaub, Marius Schneider (2011): Potassco: The Potsdam Answer Set Solving CollectionAI Commun. (2011)
    DOI: 10.3233/AIC-2011-0491
  • Maximilian Möller, Marius Schneider, Martin Wegner, Torsten Schaub (2010): Centurio, a General Game Player: Parallel, Java- and ASP-basedKünstliche Intell. (2011)
    DOI: https://doi.org/10.1007/s13218-010-0077-4
  • Martin Gebser, Holger Jost, Roland Kaminski, Philipp Obermeier, Orkunt Sabuncu, Torsten Schaub, Marius Schneider (2013): Ricochet Robots: A transverse ASP benchmarkLogic Programming and Non-Monotonic Reasoning (LPNMR) 2013
    DOI: https://doi.org/10.1007/978-3-642-40564-8_35
    ISBN: 978-3-642-40563-1
  • Benjamin Kaufmann, Torsten Schaub, Marius Schneider (2012): clasp, claspfolio, aspeed: Three Solvers from the Answer Set Solving Collection Potassco | File |
  • Marius Lindauer, Lars Kotthoff (2017): What can we learn from algorithm selection data? (Breakout Session Report)Dagstuhl Reports
  • André Biedenkapp, David Speck, Silvan Sievers, Frank Hutter, Marius Lindauer, Jendrik Seipp (2022): Learning Domain-Independent Policies for Open List SelectionProceedings of the 3rd ICAPS workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL), pp. 1-9 | File |
  • André Biedenkapp, H. Furkan Bozkurt, Theresa Eimer, Frank Hutter, Marius Lindauer (2020): Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic FrameworkEuropean Conference on Artificial Intelligence (ECAI) 2020
    DOI: 10.3233/FAIA200122
  • Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter, (2018): Practical Automated Machine Learning for the AutoML Challenge 2018International Conference on Machine Learning (ICML) 2018 AutoML Workshop | File |
  • Marius Lindauer, Frank Hutter, Holger H. Hoos, Torsten Schaub (2017): AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract)International Joint Conference on Artificial Intelligence (IJCAI) 2017
    DOI: https://doi.org/10.24963/ijcai.2017/715
  • Benjamins, Carolin and Raponi, Elena and Jankovic, Anja and van der Blom, Koen and Santoni, Maria Laura and Lindauer, Marius and Doerr, Carola (2022): PI is back! Switching Acquisition Functions in Bayesian Optimization2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems
    arXiv: 2211.01455

Projects

  • Dynamic Algorithm Configuration
    Da die Konfigurationen während der Laufzeit in Abhängigkeit vom aktuellen Zustand des Algorithmus ausgewählt werden sollten, kann es als ein Problem des Reinforcement Learning (RL) betrachtet werden, bei dem ein Agent in jedem Zeitschritt die zu verwendende Konfiguration auf der Grundlage der Leistung im letzten Schritt und des aktuellen Zustands des Algorithmus auswählt. Dies ermöglicht uns einerseits den Einsatz leistungsfähiger RL-Methoden, andererseits bringt RL auch eine Reihe von Herausforderungen mit sich, wie Instabilität, Rauschen und Ineffizienz bei der Abtastung, die bei Anwendungen wie DAC angegangen werden müssen. Daher umfasst die Forschung zu DAC auch die Forschung zu zuverlässigem, interpretierbarem, allgemeinem und schnellem Reinforcement Learning.
    Led by: Prof. Dr. Marius Lindauer
    Year: 2019
    Funding: DFG
    Duration: 2019-2023
  • Dynamic Algorithm Configuration
    As configurations should be chosen during runtime depending on the current algorithm state, it can be viewed as a reinforcement learning (RL) problem where at each timestep an agent selects the configuration to use based on the performance in the last step and the current state of the algorithm. This enables us to use powerful RL methods on one hand; on the other, RL also brings a set of challenges like instability, noise and sample inefficiency that need to be addressed in applications such as DAC. Therefore research on DAC also includes research on reliable, interpretable, general and fast reinforcement learning.
    Led by: Prof. Dr. Marius Lindauer
    Year: 2019
    Funding: DFG
    Duration: 2019-2023
  • CoyPu: Cognitive Economy Intelligence Plattform für die Resilienz wirtschaftlicher Ökosysteme
    Naturkatastrophen, Pandemien, Finanzkrisen, politische Krisen und Angebotsknappeheiten oder Nachfrageschocks propagieren sich durch offensichtliche und latente Handelsbeziehungen durch das globale ökonomische System. Dies ist eine Konsequenz der kontinuierlichen Globalisierung mit der einhergehenden Arbeitsteilung. Ziel dieses Projektes ist es diese Verbindungen offenzulegen und kaskadierende Risiken vorherzusagen um damit Unternehmen die Möglichkeit einzuräumen vorausschauend agieren zu können.
    Led by: Prof. Marius Lindauer and Prof. Maria Esther-Vidal (L3S/LUH)
    Team: InfAI, DATEV eg., eccenca GmbH, Implisense GmbH, Deutsches Institut für Wirtschaftsforschung, Leibniz Informationszentrum Technik und Naturwissenschaften, Hamburger Informatik Technologie-Center e.V., Selbstregulierung Informationswirtschaft e.V., Infineo
    Year: 2021
    Funding: Innovationswettbewerb Künstliche Intelligenz (BMWK)
    Duration: 2021-2024
  • Leibniz AI Academy
    The Leibniz AI Academy aims to develop and establish a trans-curricular and interdisciplinary micro-degree program at the Leibniz Universität Hannover (LUH), in which students from different courses of study acquire competencies in the field of Artificial Intelligence
    Led by: Prof. Dr. Marius Lindauer, Prof. Dr. Ralph Ewert, Prof. Dr. Johannes Krugel
    Year: 2021
    Funding: Bundesministerium für Bildung und Forschung (BMBF)
    Duration: 2021 - 2024
    Logo of Leibniz AI academy Logo of Leibniz AI academy
  • ERC Starting Grant: Interactive and Explainable Human-Centered AutoML
    Trust and interactivity are key factors in the future development and use of automated machine learning (AutoML), supporting developers and researchers in determining powerful task-specific machine learning pipelines, including pre-processing, predictive algorithm, their hyperparameters and--if applicable--the architecture design of deep neural networks. Although AutoML is ready for its prime time after it achieved impressive results in several machine learning (ML) applications and its efficiency improved by several orders of magnitudes in recent years, democratization of machine learning via AutoML is still not achieved. In contrast to previously purely automation-centered approaches, ixAutoML is designed with human users at its heart in several stages. First of all, the foundation of trustful use of AutoML will be based on explanations of its results and processes. Therefore, we aim for: (i) Explaining static effects of design decisions in ML pipelines optimized by state-of-the-art AutoML systems. (ii) Explaining dynamic AutoML policies for temporal aspects of dynamically adapted hyperparameters while ML models are trained. These explanations will be the base for allowing interactions, bringing the best of two worlds together: human intuition and generalization capabilities for complex systems, and efficiency of systematic optimization approaches for AutoML. Concretely, we aim for: (iii) Enabling interactions between humans and AutoML by taking human's latent knowledge into account and learning when to interact. (iv) Building first ixAutoML prototypes and showing its efficiency in the context of Industry 4.0. Perfectly aligned with the EU's AI strategy and recent efforts on interpretability in the ML community, we strongly believe that this timely human-centered ixAutoML will have a substantial impact on the democratization of machine learning.
    Led by: Prof. Dr. Marius Lindauer
    Team: AutoML
    Year: 2022
    Funding: EU
    Duration: 2022-2027
  • KISSKI: AI Service Center
    The central approach for the KISSKI project is the research on AI methods and their provision with the goal of enabling a highly available AI service center for critical and sensitive infrastructures with a focus on the fields of medicine and energy. Due to their relevance to society as a whole, medicine and the energy industry are among the future fields of application-oriented AI research in Germany. Beyond the technological developments, artificial intelligence (AI) has the potential to make a significant contribution to social progress. This is particularly true in areas where digitization processes are increasingly gaining ground and complexity is high. For both medicine and the energy industry, the pressure to innovate, but also the potential, is immense due to the availability of more and more distributed information based on a multitude of new sensors and actuators. The increasing complexity of the tasks as well as the availability of very large data sets offer a high potential for the application of AI methods in both topics.
    Led by: Prof. Dr. Marius Lindauer
    Team: AutoML
    Year: 2022
    Funding: BMBF
    Duration: 2022-2025