Marius Lindauer

167 papers A* 17A 5B 3Journal 113Unranked 23

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. J. Data-centric Mach. Learn. Res. Jannis Becktepe, Julian Dierkes, Carolin Benjamins, Aditya Mohan, David Salinas, Raghu Rajan, Frank Hutter, Holger Hoos, Marius Lindauer, Theresa Eimer
2026	J	`jnl`	Automated Data Preparation for Machine Learning: A Survey. J. Data-centric Mach. Learn. Res. Sasa Mladenovic, Marius Lindauer, Carola Doerr
2026	J	`jnl`	BONNI: Gradient-Informed Bayesian and Interior Point Optimization for Efficient Inverse Design in Nanophotonics. CoRR Yannik Mahlau, Yannick Augenstein, Tyler W. Hughes, Marius Lindauer, Bodo Rosenhahn
2026	J	`jnl`	Dynamic Hyperparameter Importance for Efficient Multi-Objective Optimization. CoRR Daphne Theodorakopoulos, Marcel Wever, Marius Lindauer
2026	J	`jnl`	Evolutionary Mapping of Neural Networks to Spatial Accelerators. CoRR Alessandro Pierro, Jonathan Timcheck, Jason Yik, Marius Lindauer, Eyke Hüllermeier, Marcel Wever
2026	A*	`conf`	HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization. AAAI Marcel Wever, Maximilian Muschalik, Fabian Fumagalli, Marius Lindauer
2026	J	`jnl`	Moments Matter:Stabilizing Policy Optimization using Return Distributions. CoRR Dennis Jabs, Aditya Mohan, Marius Lindauer
2026	J	`jnl`	Neural Attention Search Linear: Towards Adaptive Token-Level Hybrid Attention Models. CoRR Difan Deng, Andreas Bentzen Winje, Lukas Fehring, Marius Lindauer
2026	J	`jnl`	When Are RL Hyperparameters Benign? A Study in Offline Goal-Conditioned RL. CoRR Jan Malte Töpperwien, Aditya Mohan, Marius Lindauer
2025	—	`conf`	Auto-nnU-Net: Towards Automated Medical Image Segmentation. AutoML Jannis Becktepe, Leona Hennig, Steffen Oeltze-Jafra, Marius Lindauer
2025	J	`jnl`	Auto-nnU-Net: Towards Automated Medical Image Segmentation. CoRR Jannis Becktepe, Leona Hennig, Steffen Oeltze-Jafra, Marius Lindauer
2025	J	`jnl`	AutoML for Multi-Class Anomaly Compensation of Sensor Drift. CoRR Melanie Schaller, Mathis Kruse, Antonio Ortega, Marius Lindauer, Bodo Rosenhahn
2025	J	`jnl`	Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network. CoRR Theresa Eimer, Lennart Schäpermeier, André Biedenkapp, Alexander Tornede, Lars Kotthoff, Pieter Leyman, Matthias Feurer, Katharina Eggensperger, Kaitlin Maile, Tanja Tornede, Anna Kozak, Ke Xue, Marcel Wever, Mitra Baratchi, Damir Pulatov, Heike Trautmann, Haniye Kashgarani, Marius Lindauer
2025	J	`jnl`	DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning. CoRR Sarah Segel, Helena Graf, Edward Bergman, Kristina Thieme, Marcel Wever, Alexander Tornede, Frank Hutter, Marius Lindauer
2025	J	`jnl`	DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning. J. Mach. Learn. Res. Sarah Segel, Helena Graf, Edward Bergman, Kristina Thieme, Marcel Wever, Alexander Tornede, Frank Hutter, Marius Lindauer
2025	J	`jnl`	Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization. CoRR Lukas Fehring, Marcel Wever, Maximilian Spliethöver, Leona Hennig, Henning Wachsmuth, Marius Lindauer
2025	J	`jnl`	Growing with Experience: Growing Neural Networks in Deep Reinforcement Learning. CoRR Lukas Fehring, Marius Lindauer, Theresa Eimer
2025	J	`jnl`	Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks. CoRR Nick Kocher, Christian Wassermann, Leona Hennig, Jonas Seng, Holger H. Hoos, Kristian Kersting, Marius Lindauer, Matthias S. Müller
2025	B	`conf`	How Green is AutoML for Tabular Data? EDBT Felix Neutatz, Marius Lindauer, Ziawasch Abedjan
2025	J	`jnl`	HyperSHAP: Shapley Values and Interactions for Hyperparameter Importance. CoRR Marcel Wever, Maximilian Muschalik, Fabian Fumagalli, Marius Lindauer
2025	J	`jnl`	Leveraging AutoML for Sustainable Deep Learning: A Multi- Objective HPO Approach on Deep Shift Neural Networks. Trans. Mach. Learn. Res. Leona Hennig, Marius Lindauer
2025	J	`jnl`	Neural Attention Search. CoRR Difan Deng, Marius Lindauer
2025	J	`jnl`	Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach. Trans. Mach. Learn. Res. Difan Deng, Marius Lindauer
2025	J	`jnl`	Practitioner Motives to Use Different Hyperparameter Optimization Methods. ACM Trans. Comput. Hum. Interact. Niclas Kannengießer, Niklas Hasebrook, Felix Morsbach, Marc-André Zöller, Jörg K. H. Franke, Marius Lindauer, Frank Hutter, Ali Sunyaev
2025	—	`conf`	Revisiting Learning Rate Control. AutoML Micha Henheik, Theresa Eimer, Marius Lindauer
2025	J	`jnl`	Revisiting Learning Rate Control. CoRR Micha Henheik, Theresa Eimer, Marius Lindauer
2025	A	`conf`	SynthACticBench: A Capability-Based Synthetic Benchmark for Algorithm Configuration. GECCO Valentin Margraf, Anna Lappe, Marcel Wever, Carolin Benjamins, Eyke Hüllermeier, Marius Lindauer
2025	J	`jnl`	carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks. CoRR Carolin Benjamins, Helena Graf, Sarah Segel, Difan Deng, Tim Ruhkopf, Leona Hennig, Soham Basu, Neeratyoy Mallik, Edward Bergman, Deyao Chen, François Clément, Matthias Feurer, Katharina Eggensperger, Frank Hutter, Carola Doerr, Marius Lindauer
2024	J	`jnl`	AMLTK: A Modular AutoML Toolkit in Python. J. Open Source Softw. Edward Bergman, Matthias Feurer, Aron Bahram, Amir Rezaei Balef, Lennart Purucker, Sarah Segel, Marius Lindauer, Frank Hutter, Katharina Eggensperger
2024	J	`jnl`	ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. CoRR Jannis Becktepe, Julian Dierkes, Carolin Benjamins, Aditya Mohan, David Salinas, Raghu Rajan, Frank Hutter, Holger H. Hoos, Marius Lindauer, Theresa Eimer
2024	—	`conf`	Auto-sktime: Automated Time Series Forecasting. LION Marc-André Zöller, Marius Lindauer, Marco F. Huber
2024	J	`jnl`	AutoML in heavily constrained applications. VLDB J. Felix Neutatz, Marius Lindauer, Ziawasch Abedjan
2024	J	`jnl`	AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. Trans. Mach. Learn. Res. Alexander Tornede, Difan Deng, Theresa Eimer, Joseph Giovanelli, Aditya Mohan, Tim Ruhkopf, Sarah Segel, Daphne Theodorakopoulos, Tanja Tornede, Henning Wachsmuth, Marius Lindauer
2024	J	`jnl`	Automated Machine Learning For Computational Mechanics (Dagstuhl Seminar 24282). Dagstuhl Reports Elena Raponi, Lars Kotthoff, Hyunsun Alicia Kim, Marius Lindauer
2024	A	`conf`	Hyperparameter Importance Analysis for Multi-Objective AutoML. ECAI Daphne Theodorakopoulos, Frederic T. Stahl, Marius Lindauer
2024	J	`jnl`	Hyperparameter Importance Analysis for Multi-Objective AutoML. CoRR Daphne Theodorakopoulos, Frederic T. Stahl, Marius Lindauer
2024	—	`conf`	Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. GECCO Companion Carolin Benjamins, Gjorgjina Cenikj, Ana Nikolikj, Aditya Mohan, Tome Eftimov, Marius Lindauer
2024	J	`jnl`	Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. CoRR Carolin Benjamins, Gjorgjina Cenikj, Ana Nikolikj, Aditya Mohan, Tome Eftimov, Marius Lindauer
2024	A*	`conf`	Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning. AAAI Joseph Giovanelli, Alexander Tornede, Tanja Tornede, Marius Lindauer
2024	—	`ed.`	International Conference on Automated Machine Learning, 9-12 September 2024, Sorbonne Université, Paris, France. AutoML Katharina Eggensperger, Roman Garnett, Joaquin Vanschoren, Marius Lindauer, Jacob R. Gardner
2024	J	`jnl`	Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach. CoRR Difan Deng, Marius Lindauer
2024	A*	`conf`	Position: A Call to Action for a Human-Centered AutoML Paradigm. ICML Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Müller, Frank Hutter, Matthias Feurer, Bernd Bischl
2024	J	`jnl`	Position: A Call to Action for a Human-Centered AutoML Paradigm. CoRR Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Müller, Frank Hutter, Matthias Feurer, Bernd Bischl
2024	J	`jnl`	Structure in Deep Reinforcement Learning: A Survey and Open Problems. J. Artif. Intell. Res. Aditya Mohan, Amy Zhang, Marius Lindauer
2024	J	`jnl`	Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks. CoRR Leona Hennig, Tanja Tornede, Marius Lindauer
2023	J	`jnl`	AutoML in Heavily Constrained Applications. CoRR Felix Neutatz, Marius Lindauer, Ziawasch Abedjan
2023	J	`jnl`	AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. CoRR Alexander Tornede, Difan Deng, Theresa Eimer, Joseph Giovanelli, Aditya Mohan, Tim Ruhkopf, Sarah Segel, Daphne Theodorakopoulos, Tanja Tornede, Henning Wachsmuth, Marius Lindauer
2023	—	`conf`	AutoRL Hyperparameter Landscapes. AutoML Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer
2023	J	`jnl`	AutoRL Hyperparameter Landscapes. CoRR Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer
2023	B	`conf`	Automated Machine Learning for Remaining Useful Life Predictions. SMC Marc-André Zöller, Fabian Mauthe, Peter Zeiler, Marius Lindauer, Marco F. Huber
2023	J	`jnl`	Automated Machine Learning for Remaining Useful Life Predictions. CoRR Marc-André Zöller, Fabian Mauthe, Peter Zeiler, Marius Lindauer, Marco F. Huber
2023	J	`jnl`	Contextualize Me - The Case for Context in Reinforcement Learning. Trans. Mach. Learn. Res. Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, Sebastian Döhler, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
2023	J	`jnl`	Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. WIREs Data. Mining. Knowl. Discov. 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
2023	A*	`conf`	Hyperparameters in Reinforcement Learning and How To Tune Them. ICML Theresa Eimer, Marius Lindauer, Roberta Raileanu
2023	J	`jnl`	Hyperparameters in Reinforcement Learning and How To Tune Them. CoRR Theresa Eimer, Marius Lindauer, Roberta Raileanu
2023	J	`jnl`	Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning. CoRR Joseph Giovanelli, Alexander Tornede, Tanja Tornede, Marius Lindauer
2023	—	`conf`	Learning Activation Functions for Sparse Neural Networks. AutoML Mohammad Loni, Aditya Mohan, Mehdi Asadi, Marius Lindauer
2023	J	`jnl`	Learning Activation Functions for Sparse Neural Networks. CoRR Mohammad Loni, Aditya Mohan, Mehdi Asadi, Marius Lindauer
2023	J	`jnl`	MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. Trans. Mach. Learn. Res. Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer
2023	J	`jnl`	POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. Trans. Mach. Learn. Res. Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer
2023	A*	`conf`	PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning. NeurIPS Neeratyoy Mallik, Edward Bergman, Carl Hvarfner, Danny Stoll, Maciej Janowski, Marius Lindauer, Luigi Nardi, Frank Hutter
2023	J	`jnl`	PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning. CoRR Neeratyoy Mallik, Edward Bergman, Carl Hvarfner, Danny Stoll, Maciej Janowski, Marius Lindauer, Luigi Nardi, Frank Hutter
2023	—	`conf`	Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. AutoML Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer
2023	J	`jnl`	Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. CoRR Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer
2023	J	`jnl`	Structure in Reinforcement Learning: A Survey and Open Problems. CoRR Aditya Mohan, Amy Zhang, Marius Lindauer
2023	—	`conf`	Symbolic Explanations for Hyperparameter Optimization. AutoML Sarah Segel, Helena Graf, Alexander Tornede, Bernd Bischl, Marius Lindauer
2023	J	`jnl`	Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332). Dagstuhl Reports Diederick Vermetten, Martin S. Krejca, Marius Lindauer, Manuel López-Ibáñez, Katherine M. Malan
2023	—	`conf`	Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. GECCO Companion Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer
2023	J	`jnl`	auto-sktime: Automated Time Series Forecasting. CoRR Marc-André Zöller, Marius Lindauer, Marco F. Huber
2022	A*	`conf`	$\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. ICLR Carl Hvarfner, Danny Stoll, Artur L. F. Souza, Marius Lindauer, Frank Hutter, Luigi Nardi
2022	J	`jnl`	Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning. J. Mach. Learn. Res. Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter
2022	J	`jnl`	Automated Dynamic Algorithm Configuration. CoRR Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter
2022	J	`jnl`	Automated Dynamic Algorithm Configuration. J. Artif. Intell. Res. Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter
2022	J	`jnl`	Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. CoRR 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	J	`jnl`	Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. J. Artif. Intell. Res. 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	J	`jnl`	Contextualize Me - The Case for Context in Reinforcement Learning. CoRR Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
2022	J	`jnl`	DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning. CoRR René Sass, Eddie Bergman, André Biedenkapp, Frank Hutter, Marius Lindauer
2022	—	`conf`	Developing Open Source Educational Resources for Machine Learning and Data Science. Teaching ML Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl
2022	—	`conf`	Efficient Automated Deep Learning for Time Series Forecasting. ECML/PKDD (3) Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer
2022	J	`jnl`	Efficient Automated Deep Learning for Time Series Forecasting. CoRR Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer
2022	J	`jnl`	Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. CoRR Julia Moosbauer, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
2022	J	`jnl`	Hyperparameters in Contextual RL are Highly Situational. CoRR Theresa Eimer, Carolin Benjamins, Marius Lindauer
2022	—	`ed.`	International Conference on Automated Machine Learning, AutoML 2022, 25-27 July 2022, Johns Hopkins University, Baltimore, MD, USA. AutoML Isabelle Guyon, Marius Lindauer, Mihaela van der Schaar, Frank Hutter, Roman Garnett
2022	J	`jnl`	PI is back! Switching Acquisition Functions in Bayesian Optimization. CoRR Carolin Benjamins, Elena Raponi, Anja Jankovic, Koen van der Blom, Maria Laura Santoni, Marius Lindauer, Carola Doerr
2022	J	`jnl`	POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. CoRR Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer
2022	J	`jnl`	SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. J. Mach. Learn. Res. Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter
2022	—	`conf`	Searching in the Forest for Local Bayesian Optimization. Meta-Knowledge Transfer @ ECML/PKDD Difan Deng, Marius Lindauer
2022	J	`jnl`	Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. CoRR Carolin Benjamins, Anja Jankovic, Elena Raponi, Koen van der Blom, Marius Lindauer, Carola Doerr
2022	J	`jnl`	Towards Meta-learned Algorithm Selection using Implicit Fidelity Information. CoRR Aditya Mohan, Tim Ruhkopf, Marius Lindauer
2022	J	`jnl`	Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019. CoRR 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
2022	J	`jnl`	πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. CoRR Carl Hvarfner, Danny Stoll, Artur L. F. Souza, Marius Lindauer, Frank Hutter, Luigi Nardi
2021	J	`jnl`	Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. IEEE Trans. Pattern Anal. Mach. Intell. Lucas Zimmer, Marius Lindauer, Frank Hutter
2021	J	`jnl`	Automatic Risk Adaptation in Distributional Reinforcement Learning. CoRR Frederik Schubert, Theresa Eimer, Bodo Rosenhahn, Marius Lindauer
2021	J	`jnl`	Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization. CoRR Julia Guerrero-Viu, Sven Hauns, Sergio Izquierdo, Guilherme Miotto, Simon Schrodi, Andre Biedenkapp, Thomas Elsken, Difan Deng, Marius Lindauer, Frank Hutter
2021	—	`conf`	Bayesian Optimization with a Prior for the Optimum. ECML/PKDD (3) Artur L. F. Souza, Luigi Nardi, Leonardo B. Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter
2021	J	`jnl`	CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. CoRR Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
2021	A*	`conf`	DACBench: A Benchmark Library for Dynamic Algorithm Configuration. IJCAI Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer
2021	J	`jnl`	DACBench: A Benchmark Library for Dynamic Algorithm Configuration. CoRR Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer
2021	J	`jnl`	Developing Open Source Educational Resources for Machine Learning and Data Science. CoRR Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl
2021	A*	`conf`	Explaining Hyperparameter Optimization via Partial Dependence Plots. NeurIPS Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
2021	J	`jnl`	Explaining Hyperparameter Optimization via Partial Dependence Plots. CoRR Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
2021	—	`conf`	HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. NeurIPS Datasets and Benchmarks Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor H. Awad, Marius Lindauer, Frank Hutter
2021	J	`jnl`	HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. CoRR Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor H. Awad, Marius Lindauer, Frank Hutter
2021	J	`jnl`	Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. CoRR 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	A*	`conf`	Learning Heuristic Selection with Dynamic Algorithm Configuration. ICAPS David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer
2021	J	`jnl`	Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data. CoRR Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka
2021	J	`jnl`	SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. CoRR Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, René Sass, Frank Hutter
2021	J	`jnl`	Searching in the Forest for Local Bayesian Optimization. CoRR Difan Deng, Marius Lindauer
2021	A*	`conf`	Self-Paced Context Evaluation for Contextual Reinforcement Learning. ICML Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer
2021	J	`jnl`	Self-Paced Context Evaluation for Contextual Reinforcement Learning. CoRR Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer
2021	A*	`conf`	TempoRL: Learning When to Act. ICML André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer
2021	J	`jnl`	TempoRL: Learning When to Act. CoRR André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer
2021	A*	`conf`	Well-tuned Simple Nets Excel on Tabular Datasets. NeurIPS Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka
2021	J	`jnl`	Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019. IEEE Trans. Pattern Anal. Mach. Intell. 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
2020	J	`jnl`	Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. CoRR Lucas Zimmer, Marius Lindauer, Frank Hutter
2020	J	`jnl`	Auto-Sklearn 2.0: The Next Generation. CoRR Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter
2020	A	`conf`	Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework. ECAI André Biedenkapp, H. Furkan Bozkurt, Theresa Eimer, Frank Hutter, Marius Lindauer
2020	J	`jnl`	Learning Heuristic Selection with Dynamic Algorithm Configuration. CoRR David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer
2020	—	`conf`	Learning Step-Size Adaptation in CMA-ES. PPSN (1) Gresa Shala, André Biedenkapp, Noor H. Awad, Steven Adriaensen, Marius Lindauer, Frank Hutter
2020	J	`jnl`	Neural Model-based Optimization with Right-Censored Observations. CoRR Katharina Eggensperger, Kai Haase, Philipp Müller, Marius Lindauer, Frank Hutter
2020	J	`jnl`	Prior-guided Bayesian Optimization. CoRR Artur L. F. Souza, Luigi Nardi, Leonardo B. Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter
2020	J	`jnl`	Squirrel: A Switching Hyperparameter Optimizer. CoRR 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
2019	A*	`conf`	An Evolution Strategy with Progressive Episode Lengths for Playing Games. IJCAI Lior Fuks, Noor H. Awad, Frank Hutter, Marius Lindauer
2019	—	`conf`	Automated Algorithm Selection - Predict which algorithm to use! AMIR@ECIR Marius Lindauer
2019	J	`jnl`	BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters. CoRR Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Joshua Marben, Philipp Müller, Frank Hutter
2019	J	`jnl`	Best Practices for Scientific Research on Neural Architecture Search. CoRR Marius Lindauer, Frank Hutter
2019	—	`conf`	Hands-On Automated Machine Learning Tools: Auto-Sklearn and Auto-PyTorch. AMIR@ECIR Marius Lindauer
2019	J	`jnl`	Pitfalls and Best Practices in Algorithm Configuration. J. Artif. Intell. Res. Katharina Eggensperger, Marius Lindauer, Frank Hutter
2019	J	`jnl`	The algorithm selection competitions 2015 and 2017. Artif. Intell. Marius Lindauer, Jan N. van Rijn, Lars Kotthoff
2019	J	`jnl`	Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters. CoRR Marius Lindauer, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Frank Hutter
2019	—	`ch.`	Towards Automatically-Tuned Deep Neural Networks. Automated Machine Learning Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart, Maximilian Dippel, Marius Lindauer, Frank Hutter
2019	J	`jnl`	Towards White-box Benchmarks for Algorithm Control. CoRR André Biedenkapp, H. Furkan Bozkurt, Frank Hutter, Marius Lindauer
2018	J	`jnl`	A case study of algorithm selection for the traveling thief problem. J. Heuristics Markus Wagner, Marius Lindauer, Mustafa Misir, Samadhi Nallaperuma, Frank Hutter
2018	—	`conf`	CAVE: Configuration Assessment, Visualization and Evaluation. LION Andre Biedenkapp, Joshua Marben, Marius Lindauer, Frank Hutter
2018	J	`jnl`	Efficient benchmarking of algorithm configurators via model-based surrogates. Mach. Learn. Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown
2018	A*	`conf`	Neural Networks for Predicting Algorithm Runtime Distributions. IJCAI Katharina Eggensperger, Marius Lindauer, Frank Hutter
2018	—	`ch.`	Selection and Configuration of Parallel Portfolios. Handbook of Parallel Constraint Reasoning Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown
2018	J	`jnl`	The Algorithm Selection Competition Series 2015-17. CoRR Marius Lindauer, Jan N. van Rijn, Lars Kotthoff
2018	A*	`conf`	Warmstarting of Model-Based Algorithm Configuration. AAAI Marius Lindauer, Frank Hutter
2017	A*	`conf`	AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract). IJCAI Marius Lindauer, Frank Hutter, Holger H. Hoos, Torsten Schaub
2017	J	`jnl`	Automatic construction of parallel portfolios via algorithm configuration. Artif. Intell. Marius Lindauer, Holger H. Hoos, Kevin Leyton-Brown, Torsten Schaub
2017	J	`jnl`	Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates. CoRR Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown
2017	A*	`conf`	Efficient Parameter Importance Analysis via Ablation with Surrogates. AAAI Andre Biedenkapp, Marius Lindauer, Katharina Eggensperger, Frank Hutter, Chris Fawcett, Holger H. Hoos
2017	B	`conf`	Improving local search in a minimum vertex cover solver for classes of networks. CEC Markus Wagner, Tobias Friedrich, Marius Lindauer
2017	—	`conf`	Open Algorithm Selection Challenge 2017: Setup and Scenarios. OASC Marius Lindauer, Jan N. van Rijn, Lars Kotthoff
2017	J	`jnl`	Pitfalls and Best Practices in Algorithm Configuration. CoRR Katharina Eggensperger, Marius Lindauer, Frank Hutter
2017	J	`jnl`	Predicting Runtime Distributions using Deep Neural Networks. CoRR Katharina Eggensperger, Marius Lindauer, Frank Hutter
2017	J	`jnl`	The Configurable SAT Solver Challenge (CSSC). Artif. Intell. Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger H. Hoos, Kevin Leyton-Brown
2017	J	`jnl`	Warmstarting of Model-based Algorithm Configuration. CoRR Marius Lindauer, Frank Hutter
2016	J	`jnl`	A case study of algorithm selection for the traveling thief problem. CoRR Markus Wagner, Marius Lindauer, Mustafa Misir, Samadhi Nallaperuma, Frank Hutter
2016	J	`jnl`	ASlib: A benchmark library for algorithm selection. Artif. Intell. Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren
2016	—	`conf`	An Empirical Study of Per-instance Algorithm Scheduling. LION Marius Lindauer, Rolf-David Bergdoll, Frank Hutter
2016	A	`conf`	SpyBug: Automated Bug Detection in the Configuration Space of SAT Solvers. SAT Norbert Manthey, Marius Lindauer
2015	J	`jnl`	ASlib: A Benchmark Library for Algorithm Selection. CoRR 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	—	`ed.`	Algorithm Configuration, Papers from the 2015 AAAI Workshop, Austin, Texas, USA, January 26, 2015. AAAI Workshop: Algorithm Configuration Frank Hutter, Marius Lindauer, Yuri Malitsky
2015	—	`—`	Algorithm selection, scheduling and configuration of Boolean constraint solvers Marius Lindauer
2015	—	`conf`	AutoFolio: Algorithm Configuration for Algorithm Selection. AAAI Workshop: Algorithm Configuration Marius Lindauer, Holger H. Hoos, Frank Hutter, Torsten Schaub
2015	J	`jnl`	AutoFolio: An Automatically Configured Algorithm Selector. J. Artif. Intell. Res. Marius Lindauer, Holger H. Hoos, Frank Hutter, Torsten Schaub
2015	—	`conf`	From Sequential Algorithm Selection to Parallel Portfolio Selection. LION Marius Lindauer, Holger H. Hoos, Frank Hutter
2015	J	`jnl`	Reports from the 2015 AAAI Workshop Program. AI Mag. Stefano V. Albrecht, J. Christopher Beck, David L. Buckeridge, Adi Botea, Cornelia Caragea, Chi-Hung Chi, Theodoros Damoulas, Bistra Dilkina, Eric Eaton, Pooyan Fazli, Sam Ganzfried, Marius Lindauer, Marlos C. Machado, Yuri Malitsky, Gary Marcus, Sebastiaan A. Meijer, Francesca Rossi, Arash Shaban-Nejad, Sylvie Thiébaux, Manuela M. Veloso, Toby Walsh, Can Wang, Jie Zhang, Yu Zheng
2015	A	`conf`	SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers. SAT Stefan Falkner, Marius Lindauer, Frank Hutter
2015	J	`jnl`	The Configurable SAT Solver Challenge (CSSC). CoRR Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger H. Hoos, Kevin Leyton-Brown
2015	J	`jnl`	aspeed: Solver scheduling via answer set programming. Theory Pract. Log. Program. Holger H. Hoos, Roland Kaminski, Marius Lindauer, Torsten Schaub
2014	—	`conf`	AClib: A Benchmark Library for Algorithm Configuration. LION Frank Hutter, Manuel López-Ibáñez, Chris Fawcett, Marius Lindauer, Holger H. Hoos, Kevin Leyton-Brown, Thomas Stützle
2014	J	`jnl`	Solver Scheduling via Answer Set Programming. CoRR Holger H. Hoos, Roland Kaminski, Marius Lindauer, Torsten Schaub
2014	J	`jnl`	claspfolio 2: Advances in Algorithm Selection for Answer Set Programming. CoRR Holger H. Hoos, Marius Lindauer, Torsten Schaub
2014	J	`jnl`	claspfolio 2: Advances in Algorithm Selection for Answer Set Programming. Theory Pract. Log. Program. Holger H. Hoos, Marius Lindauer, Torsten Schaub