Marius Lindauer

ORCID

Publication Activity (10 Years)

Years Active: 2014-2023
Publications (10 Years): 111

Top Venues

CoRR

J. Artif. Intell. Res.

AutoML

IJCAI

Publications

Marc-André Zöller, Fabian Mauthe, Peter Zeiler, Marius Lindauer, Marco F. Huber
Automated Machine Learning for Remaining Useful Life Predictions. CoRR (2023)
Alexander Tornede, Difan Deng, Theresa Eimer, Joseph Giovanelli, Aditya Mohan, Tim Ruhkopf, Sarah Segel, Daphne Theodorakopoulos, Tanja Tornede, Henning Wachsmuth, Marius Lindauer
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. CoRR (2023)
Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer
AutoRL Hyperparameter Landscapes. AutoML (2023)
Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer
MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. Trans. Mach. Learn. Res. 2023 (2023)
Felix Neutatz, Marius Lindauer, Ziawasch Abedjan
AutoML in Heavily Constrained Applications. CoRR (2023)
Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. AutoML (2023)
Sarah Segel, Helena Graf, Alexander Tornede, Bernd Bischl, Marius Lindauer
Symbolic Explanations for Hyperparameter Optimization. AutoML (2023)
Joseph Giovanelli, Alexander Tornede, Tanja Tornede, Marius Lindauer
Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning. CoRR (2023)
Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer
AutoRL Hyperparameter Landscapes. CoRR (2023)
Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer
Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. GECCO Companion (2023)
Theresa Eimer, Marius Lindauer, Roberta Raileanu
Hyperparameters in Reinforcement Learning and How To Tune Them. CoRR (2023)
Aditya Mohan, Amy Zhang, Marius Lindauer
Structure in Reinforcement Learning: A Survey and Open Problems. CoRR (2023)
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
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. WIREs Data. Mining. Knowl. Discov. 13 (2) (2023)
Marc-André Zöller, Fabian Mauthe, Peter Zeiler, Marius Lindauer, Marco F. Huber
Automated Machine Learning for Remaining Useful Life Predictions. SMC (2023)
Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. CoRR (2023)
Mohammad Loni, Aditya Mohan, Mehdi Asadi, Marius Lindauer
Learning Activation Functions for Sparse Neural Networks. CoRR (2023)
Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023)
Mohammad Loni, Aditya Mohan, Mehdi Asadi, Marius Lindauer
Learning Activation Functions for Sparse Neural Networks. AutoML (2023)
Theresa Eimer, Marius Lindauer, Roberta Raileanu
Hyperparameters in Reinforcement Learning and How To Tune Them. ICML (2023)
Neeratyoy Mallik, Edward Bergman, Carl Hvarfner, Danny Stoll, Maciej Janowski, Marius Lindauer, Luigi Nardi, Frank Hutter
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning. NeurIPS (2023)
Neeratyoy Mallik, Edward Bergman, Carl Hvarfner, Danny Stoll, Maciej Janowski, Marius Lindauer, Luigi Nardi, Frank Hutter
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning. CoRR (2023)
Marc-André Zöller, Marius Lindauer, Marco F. Huber
auto-sktime: Automated Time Series Forecasting. CoRR (2023)
Julia Moosbauer, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. CoRR (2022)
Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter
Automated Dynamic Algorithm Configuration. CoRR (2022)
Theresa Eimer, Carolin Benjamins, Marius Lindauer
Hyperparameters in Contextual RL are Highly Situational. CoRR (2022)
Carolin Benjamins, Elena Raponi, Anja Jankovic, Koen van der Blom, Maria Laura Santoni, Marius Lindauer, Carola Doerr
PI is back! Switching Acquisition Functions in Bayesian Optimization. CoRR (2022)
Carolin Benjamins, Anja Jankovic, Elena Raponi, Koen van der Blom, Marius Lindauer, Carola Doerr
Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. CoRR (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. CoRR (2022)
Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter
Automated Dynamic Algorithm Configuration. J. Artif. Intell. Res. 75 (2022)
Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. CoRR (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. J. Mach. Learn. Res. 23 (2022)
René Sass, Eddie Bergman, André Biedenkapp, Frank Hutter, Marius Lindauer
DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning. CoRR (2022)
Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl
Developing Open Source Educational Resources for Machine Learning and Data Science. Teaching ML (2022)
Carl Hvarfner, Danny Stoll, Artur L. F. Souza, Marius Lindauer, Frank Hutter, Luigi Nardi
$\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. ICLR (2022)
Zhengying Liu, Adrien Pavao, Zhen Xu, Sergio Escalera, Fabio Ferreira, Isabelle Guyon, Sirui Hong, Frank Hutter, Rongrong Ji, Júlio C. S. Jacques Júnior, Ge Li, Marius Lindauer, Zhipeng Luo, Meysam Madadi, Thomas Nierhoff, Kangning Niu, Chunguang Pan, Danny Stoll, Sébastien Treguer, Jin Wang, Peng Wang, Chenglin Wu, Youcheng Xiong, Arber Zela, Yang Zhang
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019. CoRR (2022)
Carl Hvarfner, Danny Stoll, Artur L. F. Souza, Marius Lindauer, Frank Hutter, Luigi Nardi
πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. CoRR (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. J. Artif. Intell. Res. 74 (2022)
Aditya Mohan, Tim Ruhkopf, Marius Lindauer
Towards Meta-learned Algorithm Selection using Implicit Fidelity Information. CoRR (2022)
Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning. J. Mach. Learn. Res. 23 (2022)
Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer
Efficient Automated Deep Learning for Time Series Forecasting. CoRR (2022)
Difan Deng, Marius Lindauer
Searching in the Forest for Local Bayesian Optimization. Meta-Knowledge Transfer @ ECML/PKDD (2022)
Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer
Efficient Automated Deep Learning for Time Series Forecasting. ECML/PKDD (3) (2022)
Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
Contextualize Me - The Case for Context in Reinforcement Learning. CoRR (2022)
Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
Explaining Hyperparameter Optimization via Partial Dependence Plots. CoRR (2021)
Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka
Well-tuned Simple Nets Excel on Tabular Datasets. NeurIPS (2021)
Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer
DACBench: A Benchmark Library for Dynamic Algorithm Configuration. IJCAI (2021)
André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer
TempoRL: Learning When to Act. ICML (2021)
André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer
TempoRL: Learning When to Act. CoRR (2021)
Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor H. Awad, Marius Lindauer, Frank Hutter
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. CoRR (2021)
Difan Deng, Marius Lindauer
Searching in the Forest for Local Bayesian Optimization. CoRR (2021)
David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer
Learning Heuristic Selection with Dynamic Algorithm Configuration. ICAPS (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. CoRR (2021)
Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka
Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data. CoRR (2021)
Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer
DACBench: A Benchmark Library for Dynamic Algorithm Configuration. CoRR (2021)
Artur L. F. Souza, Luigi Nardi, Leonardo B. Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter
Bayesian Optimization with a Prior for the Optimum. ECML/PKDD (3) (2021)
Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. CoRR (2021)
Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
Explaining Hyperparameter Optimization via Partial Dependence Plots. NeurIPS (2021)
Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, René Sass, Frank Hutter
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. CoRR (2021)
Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor H. Awad, Marius Lindauer, Frank Hutter
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. NeurIPS Datasets and Benchmarks (2021)
Zhengying Liu, Adrien Pavao, Zhen Xu, Sergio Escalera, Fabio Ferreira, Isabelle Guyon, Sirui Hong, Frank Hutter, Rongrong Ji, Júlio C. S. Jacques Júnior, Ge Li, Marius Lindauer, Zhipeng Luo, Meysam Madadi, Thomas Nierhoff, Kangning Niu, Chunguang Pan, Danny Stoll, Sébastien Treguer, Jin Wang, Peng Wang, Chenglin Wu, Youcheng Xiong, Arber Zela, Yang Zhang
Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019. IEEE Trans. Pattern Anal. Mach. Intell. 43 (9) (2021)
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
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. CoRR (2021)
Lucas Zimmer, Marius Lindauer, Frank Hutter
Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. IEEE Trans. Pattern Anal. Mach. Intell. 43 (9) (2021)
Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer
Self-Paced Context Evaluation for Contextual Reinforcement Learning. CoRR (2021)
Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl
Developing Open Source Educational Resources for Machine Learning and Data Science. CoRR (2021)
Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer
Self-Paced Context Evaluation for Contextual Reinforcement Learning. ICML (2021)
Frederik Schubert, Theresa Eimer, Bodo Rosenhahn, Marius Lindauer
Automatic Risk Adaptation in Distributional Reinforcement Learning. CoRR (2021)
David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer
Learning Heuristic Selection with Dynamic Algorithm Configuration. CoRR (2020)
André Biedenkapp, H. Furkan Bozkurt, Theresa Eimer, Frank Hutter, Marius Lindauer
Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework. ECAI (2020)
Katharina Eggensperger, Kai Haase, Philipp Müller, Marius Lindauer, Frank Hutter
Neural Model-based Optimization with Right-Censored Observations. CoRR (2020)
Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter
Auto-Sklearn 2.0: The Next Generation. CoRR (2020)
Lucas Zimmer, Marius Lindauer, Frank Hutter
Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. CoRR (2020)
Noor H. Awad, Gresa Shala, Difan Deng, Neeratyoy Mallik, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Diederick Vermetten, Hao Wang, Carola Doerr, Marius Lindauer, Frank Hutter
Squirrel: A Switching Hyperparameter Optimizer. CoRR (2020)
Gresa Shala, André Biedenkapp, Noor H. Awad, Steven Adriaensen, Marius Lindauer, Frank Hutter
Learning Step-Size Adaptation in CMA-ES. PPSN (1) (2020)
Artur L. F. Souza, Luigi Nardi, Leonardo B. Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter
Prior-guided Bayesian Optimization. CoRR (2020)
Marius Lindauer, Jan N. van Rijn, Lars Kotthoff
The algorithm selection competitions 2015 and 2017. Artif. Intell. 272 (2019)
Marius Lindauer, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Frank Hutter
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters. CoRR (2019)
Marius Lindauer
Hands-On Automated Machine Learning Tools: Auto-Sklearn and Auto-PyTorch. AMIR@ECIR (2019)
Katharina Eggensperger, Marius Lindauer, Frank Hutter
Pitfalls and Best Practices in Algorithm Configuration. J. Artif. Intell. Res. 64 (2019)
Marius Lindauer, Frank Hutter
Best Practices for Scientific Research on Neural Architecture Search. CoRR (2019)
Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Joshua Marben, Philipp Müller, Frank Hutter
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters. CoRR (2019)
André Biedenkapp, H. Furkan Bozkurt, Frank Hutter, Marius Lindauer
Towards White-box Benchmarks for Algorithm Control. CoRR (2019)
Marius Lindauer
Automated Algorithm Selection - Predict which algorithm to use! AMIR@ECIR (2019)
Lior Fuks, Noor H. Awad, Frank Hutter, Marius Lindauer
An Evolution Strategy with Progressive Episode Lengths for Playing Games. IJCAI (2019)
Andre Biedenkapp, Joshua Marben, Marius Lindauer, Frank Hutter
CAVE: Configuration Assessment, Visualization and Evaluation. LION (2018)
Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown
Efficient benchmarking of algorithm configurators via model-based surrogates. Mach. Learn. 107 (1) (2018)
Katharina Eggensperger, Marius Lindauer, Frank Hutter
Neural Networks for Predicting Algorithm Runtime Distributions. IJCAI (2018)
Markus Wagner, Marius Lindauer, Mustafa Misir, Samadhi Nallaperuma, Frank Hutter
A case study of algorithm selection for the traveling thief problem. J. Heuristics 24 (3) (2018)
Marius Lindauer, Frank Hutter
Warmstarting of Model-Based Algorithm Configuration. AAAI (2018)
Marius Lindauer, Jan N. van Rijn, Lars Kotthoff
The Algorithm Selection Competition Series 2015-17. CoRR (2018)
Katharina Eggensperger, Marius Lindauer, Frank Hutter
Predicting Runtime Distributions using Deep Neural Networks. CoRR (2017)
Katharina Eggensperger, Marius Lindauer, Frank Hutter
Pitfalls and Best Practices in Algorithm Configuration. CoRR (2017)
Marius Lindauer, Frank Hutter
Warmstarting of Model-based Algorithm Configuration. CoRR (2017)
Marius Lindauer, Frank Hutter, Holger H. Hoos, Torsten Schaub
AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract). IJCAI (2017)
Marius Lindauer, Holger H. Hoos, Kevin Leyton-Brown, Torsten Schaub
Automatic construction of parallel portfolios via algorithm configuration. Artif. Intell. 244 (2017)
Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates. CoRR (2017)
Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger H. Hoos, Kevin Leyton-Brown
The Configurable SAT Solver Challenge (CSSC). Artif. Intell. 243 (2017)
Andre Biedenkapp, Marius Lindauer, Katharina Eggensperger, Frank Hutter, Chris Fawcett, Holger H. Hoos
Efficient Parameter Importance Analysis via Ablation with Surrogates. AAAI (2017)
Marius Lindauer, Jan N. van Rijn, Lars Kotthoff
Open Algorithm Selection Challenge 2017: Setup and Scenarios. OASC (2017)
Markus Wagner, Tobias Friedrich, Marius Lindauer
Improving local search in a minimum vertex cover solver for classes of networks. CEC (2017)
Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren
ASlib: A benchmark library for algorithm selection. Artif. Intell. 237 (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Marius Lindauer