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ORCIDMarius Lindauer
Marius Schneider 0001 – Marius Thomas Schneider
Person information
- affiliation (since 2019): Leibniz University Hannover, Institute of Information Processing, Germany
- affiliation (2014-2019): University of Freiburg, Machine Learning Lab, Germany
- affiliation (PhD 2015): University of Potsdam, Institute of Computer Science, Germany
Other persons with the same name
- Marius Schneider 0002
![[0000-0002-8405-4131]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-8405-4131")
— Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany (and 1 more)
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2020 – today
- 2025
[c48]Felix Neutatz, Marius Lindauer, Ziawasch Abedjan:
How Green is AutoML for Tabular Data? EDBT 2025: 350-363- 2024
- [j26]Aditya Mohan, Amy Zhang, Marius Lindauer:
Structure in Deep Reinforcement Learning: A Survey and Open Problems. J. Artif. Intell. Res. 79: 1167-1236 (2024) - [j25]Edward Bergman, Matthias Feurer, Aron Bahram, Amir Rezaei Balef, Lennart Purucker, Sarah Segel, Marius Lindauer, Frank Hutter, Katharina Eggensperger:
AMLTK: A Modular AutoML Toolkit in Python. J. Open Source Softw. 9(100): 6367 (2024) - [j24]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. Trans. Mach. Learn. Res. 2024 (2024) - [j23]Felix Neutatz, Marius Lindauer, Ziawasch Abedjan:
AutoML in heavily constrained applications. VLDB J. 33(4): 957-979 (2024) - [c47]Joseph Giovanelli, Alexander Tornede, Tanja Tornede, Marius Lindauer:
Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning. AAAI 2024: 12172-12180 - [c46]Daphne Theodorakopoulos, Frederic T. Stahl, Marius Lindauer:
Hyperparameter Importance Analysis for Multi-Objective AutoML. ECAI 2024: 1100-1107 - [c45]Carolin Benjamins, Gjorgjina Cenikj, Ana Nikolikj, Aditya Mohan, Tome Eftimov, Marius Lindauer:
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. GECCO Companion 2024: 563-566 - [c44]Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Müller, Frank Hutter, Matthias Feurer, Bernd Bischl:
Position: A Call to Action for a Human-Centered AutoML Paradigm. ICML 2024 - [i66]Leona Hennig, Tanja Tornede, Marius Lindauer:
Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks. CoRR abs/2404.01965 (2024) - [i65]Daphne Theodorakopoulos, Frederic T. Stahl, Marius Lindauer:
Hyperparameter Importance Analysis for Multi-Objective AutoML. CoRR abs/2405.07640 (2024) - [i64]Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Müller, Frank Hutter, Matthias Feurer, Bernd Bischl:
Position: A Call to Action for a Human-Centered AutoML Paradigm. CoRR abs/2406.03348 (2024) - [i63]Difan Deng, Marius Lindauer:
Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach. CoRR abs/2406.05088 (2024) - [i62]Carolin Benjamins, Gjorgjina Cenikj, Ana Nikolikj, Aditya Mohan, Tome Eftimov, Marius Lindauer:
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. CoRR abs/2407.13513 (2024) - [i61]Jannis Becktepe, Julian Dierkes, Carolin Benjamins, Aditya Mohan, David Salinas, Raghu Rajan, Frank Hutter, Holger H. Hoos, Marius Lindauer, Theresa Eimer:
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. CoRR abs/2409.18827 (2024) - 2023
- [j22]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. Trans. Mach. Learn. Res. 2023 (2023) - [j21]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) - [j20]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) - [j19]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) - [c43]Sarah Segel, Helena Graf, Alexander Tornede, Bernd Bischl, Marius Lindauer:
Symbolic Explanations for Hyperparameter Optimization. AutoML 2023: 2/1-22 - [c42]Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer:
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. AutoML 2023: 6/1-50 - [c41]Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer:
AutoRL Hyperparameter Landscapes. AutoML 2023: 13/1-27 - [c40]Mohammad Loni, Aditya Mohan, Mehdi Asadi, Marius Lindauer:
Learning Activation Functions for Sparse Neural Networks. AutoML 2023: 16/1-19 - [c39]Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer:
Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. GECCO Companion 2023: 483-486 - [c38]Theresa Eimer, Marius Lindauer, Roberta Raileanu:
Hyperparameters in Reinforcement Learning and How To Tune Them. ICML 2023: 9104-9149 - [c37]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 - [c36]Marc-André Zöller, Fabian Mauthe, Peter Zeiler, Marius Lindauer, Marco F. Huber:
Automated Machine Learning for Remaining Useful Life Predictions. SMC 2023: 2907-2912 - [i60]Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer:
AutoRL Hyperparameter Landscapes. CoRR abs/2304.02396 (2023) - [i59]Mohammad Loni, Aditya Mohan, Mehdi Asadi, Marius Lindauer:
Learning Activation Functions for Sparse Neural Networks. CoRR abs/2305.10964 (2023) - [i58]Theresa Eimer, Marius Lindauer, Roberta Raileanu:
Hyperparameters in Reinforcement Learning and How To Tune Them. CoRR abs/2306.01324 (2023) - [i57]Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer:
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. CoRR abs/2306.04262 (2023) - [i56]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 abs/2306.08107 (2023) - [i55]Marc-André Zöller, Fabian Mauthe, Peter Zeiler, Marius Lindauer, Marco F. Huber:
Automated Machine Learning for Remaining Useful Life Predictions. CoRR abs/2306.12215 (2023) - [i54]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 abs/2306.12370 (2023) - [i53]Aditya Mohan, Amy Zhang, Marius Lindauer:
Structure in Reinforcement Learning: A Survey and Open Problems. CoRR abs/2306.16021 (2023) - [i52]Felix Neutatz, Marius Lindauer, Ziawasch Abedjan:
AutoML in Heavily Constrained Applications. CoRR abs/2306.16913 (2023) - [i51]Joseph Giovanelli, Alexander Tornede, Tanja Tornede, Marius Lindauer:
Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning. CoRR abs/2309.03581 (2023) - [i50]Marc-André Zöller, Marius Lindauer, Marco F. Huber:
auto-sktime: Automated Time Series Forecasting. CoRR abs/2312.08528 (2023) - [i49]Diederick Vermetten, Martin S. Krejca, Marius Lindauer, Manuel López-Ibáñez, Katherine M. Malan:
Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332). Dagstuhl Reports 13(8): 46-70 (2023) - 2022
- [j18]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: 517-568 (2022) - [j17]Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter:
Automated Dynamic Algorithm Configuration. J. Artif. Intell. Res. 75: 1633-1699 (2022) - [j16]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: 54:1-54:9 (2022) - [j15]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: 261:1-261:61 (2022) - [c35]Difan Deng, Marius Lindauer:
Searching in the Forest for Local Bayesian Optimization. Meta-Knowledge Transfer @ ECML/PKDD 2022: 38-50 - [c34]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 - [c33]Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer:
Efficient Automated Deep Learning for Time Series Forecasting. ECML/PKDD (3) 2022: 664-680 - [c32]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: 1-6 - [e2]Isabelle Guyon, Marius Lindauer, Mihaela van der Schaar, Frank Hutter, Roman Garnett:
International Conference on Automated Machine Learning, AutoML 2022, 25-27 July 2022, Johns Hopkins University, Baltimore, MD, USA. Proceedings of Machine Learning Research 188, PMLR 2022 [contents] - [i48]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 abs/2201.03801 (2022) - [i47]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 abs/2201.03916 (2022) - [i46]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 abs/2202.04500 (2022) - [i45]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 abs/2204.11051 (2022) - [i44]Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer:
Efficient Automated Deep Learning for Time Series Forecasting. CoRR abs/2205.05511 (2022) - [i43]Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer:
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. CoRR abs/2205.11357 (2022) - [i42]Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter:
Automated Dynamic Algorithm Configuration. CoRR abs/2205.13881 (2022) - [i41]Aditya Mohan, Tim Ruhkopf, Marius Lindauer:
Towards Meta-learned Algorithm Selection using Implicit Fidelity Information. CoRR abs/2206.03130 (2022) - [i40]René Sass, Eddie Bergman, André Biedenkapp, Frank Hutter, Marius Lindauer:
DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning. CoRR abs/2206.03493 (2022) - [i39]Julia Moosbauer, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl:
Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. CoRR abs/2206.05447 (2022) - [i38]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 abs/2211.01455 (2022) - [i37]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 abs/2211.09678 (2022) - [i36]Theresa Eimer, Carolin Benjamins, Marius Lindauer:
Hyperparameters in Contextual RL are Highly Situational. CoRR abs/2212.10876 (2022) - 2021
- [j14]Lucas Zimmer, Marius Lindauer, Frank Hutter:
Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. IEEE Trans. Pattern Anal. Mach. Intell. 43(9): 3079-3090 (2021) - [j13]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): 3108-3125 (2021) - [c31]David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer:
Learning Heuristic Selection with Dynamic Algorithm Configuration. ICAPS 2021: 597-605 - [c30]André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer:
TempoRL: Learning When to Act. ICML 2021: 914-924 - [c29]Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer:
Self-Paced Context Evaluation for Contextual Reinforcement Learning. ICML 2021: 2948-2958 - [c28]Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer:
DACBench: A Benchmark Library for Dynamic Algorithm Configuration. IJCAI 2021: 1668-1674 - [c27]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 - [c26]Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl:
Explaining Hyperparameter Optimization via Partial Dependence Plots. NeurIPS 2021: 2280-2291 - [c25]Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka:
Well-tuned Simple Nets Excel on Tabular Datasets. NeurIPS 2021: 23928-23941 - [c24]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: 265-296 - [i35]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 abs/2105.01015 (2021) - [i34]Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer:
DACBench: A Benchmark Library for Dynamic Algorithm Configuration. CoRR abs/2105.08541 (2021) - [i33]Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer:
Self-Paced Context Evaluation for Contextual Reinforcement Learning. CoRR abs/2106.05110 (2021) - [i32]André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer:
TempoRL: Learning When to Act. CoRR abs/2106.05262 (2021) - [i31]Frederik Schubert, Theresa Eimer, Bodo Rosenhahn, Marius Lindauer:
Automatic Risk Adaptation in Distributional Reinforcement Learning. CoRR abs/2106.06317 (2021) - [i30]Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka:
Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data. CoRR abs/2106.11189 (2021) - [i29]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 abs/2107.05847 (2021) - [i28]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 abs/2107.14330 (2021) - [i27]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 abs/2109.06716 (2021) - [i26]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 abs/2109.09831 (2021) - [i25]Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer:
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. CoRR abs/2110.02102 (2021) - [i24]Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl:
Explaining Hyperparameter Optimization via Partial Dependence Plots. CoRR abs/2111.04820 (2021) - [i23]Difan Deng, Marius Lindauer:
Searching in the Forest for Local Bayesian Optimization. CoRR abs/2111.05834 (2021) - 2020
- [c23]André Biedenkapp, H. Furkan Bozkurt, Theresa Eimer, Frank Hutter, Marius Lindauer:
Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework. ECAI 2020: 427-434 - [c22]Gresa Shala, André Biedenkapp, Noor H. Awad, Steven Adriaensen, Marius Lindauer, Frank Hutter:
Learning Step-Size Adaptation in CMA-ES. PPSN (1) 2020: 691-706 - [i22]David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer:
Learning Heuristic Selection with Dynamic Algorithm Configuration. CoRR abs/2006.08246 (2020) - [i21]Lucas Zimmer, Marius Lindauer, Frank Hutter:
Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. CoRR abs/2006.13799 (2020) - [i20]Artur L. F. Souza, Luigi Nardi, Leonardo B. Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter:
Prior-guided Bayesian Optimization. CoRR abs/2006.14608 (2020) - [i19]Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter:
Auto-Sklearn 2.0: The Next Generation. CoRR abs/2007.04074 (2020) - [i18]Katharina Eggensperger, Kai Haase, Philipp Müller, Marius Lindauer, Frank Hutter:
Neural Model-based Optimization with Right-Censored Observations. CoRR abs/2009.13828 (2020) - [i17]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 abs/2012.08180 (2020)
2010 – 2019
- 2019
- [j12]Marius Lindauer, Jan N. van Rijn, Lars Kotthoff:
The algorithm selection competitions 2015 and 2017. Artif. Intell. 272: 86-100 (2019) - [j11]Katharina Eggensperger, Marius Lindauer, Frank Hutter:
Pitfalls and Best Practices in Algorithm Configuration. J. Artif. Intell. Res. 64: 861-893 (2019) - [c21]Marius Lindauer:
Automated Algorithm Selection - Predict which algorithm to use! AMIR@ECIR 2019: 10 - [c20]Marius Lindauer:
Hands-On Automated Machine Learning Tools: Auto-Sklearn and Auto-PyTorch. AMIR@ECIR 2019: 72 - [c19]Lior Fuks, Noor H. Awad, Frank Hutter, Marius Lindauer:
An Evolution Strategy with Progressive Episode Lengths for Playing Games. IJCAI 2019: 1234-1240 - [p2]Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart, Maximilian Dippel, Marius Lindauer, Frank Hutter:
Towards Automatically-Tuned Deep Neural Networks. Automated Machine Learning 2019: 135-149 - [i16]André Biedenkapp, H. Furkan Bozkurt, Frank Hutter, Marius Lindauer:
Towards White-box Benchmarks for Algorithm Control. CoRR abs/1906.07644 (2019) - [i15]Marius Lindauer, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Frank Hutter:
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters. CoRR abs/1908.06674 (2019) - [i14]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 abs/1908.06756 (2019) - [i13]Marius Lindauer, Frank Hutter:
Best Practices for Scientific Research on Neural Architecture Search. CoRR abs/1909.02453 (2019) - 2018
- [j10]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): 295-320 (2018) - [j9]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): 15-41 (2018) - [c18]Marius Lindauer, Frank Hutter:
Warmstarting of Model-Based Algorithm Configuration. AAAI 2018: 1355-1362 - [c17]Katharina Eggensperger, Marius Lindauer, Frank Hutter:
Neural Networks for Predicting Algorithm Runtime Distributions. IJCAI 2018: 1442-1448 - [c16]Andre Biedenkapp, Joshua Marben, Marius Lindauer, Frank Hutter:
CAVE: Configuration Assessment, Visualization and Evaluation. LION 2018: 115-130 - [p1]Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Selection and Configuration of Parallel Portfolios. Handbook of Parallel Constraint Reasoning 2018: 583-615 - [i12]Marius Lindauer, Jan N. van Rijn, Lars Kotthoff:
The Algorithm Selection Competition Series 2015-17. CoRR abs/1805.01214 (2018) - 2017
- [j8]Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger H. Hoos, Kevin Leyton-Brown:
The Configurable SAT Solver Challenge (CSSC). Artif. Intell. 243: 1-25 (2017) - [j7]Marius Lindauer, Holger H. Hoos, Kevin Leyton-Brown, Torsten Schaub:
Automatic construction of parallel portfolios via algorithm configuration. Artif. Intell. 244: 272-290 (2017) - [c15]Andre Biedenkapp, Marius Lindauer, Katharina Eggensperger, Frank Hutter, Chris Fawcett, Holger H. Hoos:
Efficient Parameter Importance Analysis via Ablation with Surrogates. AAAI 2017: 773-779 - [c14]