Lindauer, Marius

46 publications

AutoML 2025 Auto-nnU-Net: Towards Automated Medical Image Segmentation Jannis Becktepe, Leona Hennig, Steffen Oeltze-Jafra, Marius Lindauer

TMLR 2025 Leveraging AutoML for Sustainable Deep Learning: A Multi- Objective HPO Approach on Deep Shift Neural Networks Leona Hennig, Marius Lindauer

NeurIPS 2025 Neural Attention Search Difan Deng, Marius Lindauer

TMLR 2025 Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach Difan Deng, Marius Lindauer

AutoML 2025 Revisiting Learning Rate Control Micha Henheik, Theresa Eimer, Marius Lindauer

TMLR 2024 AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks Alexander Tornede, Difan Deng, Theresa Eimer, Joseph Giovanelli, Aditya Mohan, Tim Ruhkopf, Sarah Segel, Daphne Theodorakopoulos, Tanja Tornede, Henning Wachsmuth, Marius Lindauer

NeurIPSW 2024 Bayesian Optimization for Protein Sequence Design: Back to Simplicity with Gaussian Processes Carolin Benjamins, Shikha Surana, Oliver Bent, Marius Lindauer, Paul Duckworth

AAAI 2024 Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning Joseph Giovanelli, Alexander Tornede, Tanja Tornede, Marius Lindauer

NeurIPSW 2024 Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach Difan Deng, Marius Lindauer

ICML 2024 Position: A Call to Action for a Human-Centered AutoML Paradigm Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas C Mueller, Frank Hutter, Matthias Feurer, Bernd Bischl

JAIR 2024 Structure in Deep Reinforcement Learning: A Survey and Open Problems Aditya Mohan, Amy Zhang, Marius Lindauer

ICLRW 2024 Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks Leona Hennig, Tanja Tornede, Marius Lindauer

AutoML 2023 AutoRL Hyperparameter Landscapes Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer

TMLR 2023 Contextualize Me – The Case for Context in Reinforcement Learning Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, Sebastian Döhler, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer

ICML 2023 Hyperparameters in Reinforcement Learning and How to Tune Them Theresa Eimer, Marius Lindauer, Roberta Raileanu

AutoML 2023 Learning Activation Functions for Sparse Neural Networks Mohammad Loni, Aditya Mohan, Mehdi Asadi, Marius Lindauer

TMLR 2023 MASIF: Meta-Learned Algorithm Selection Using Implicit Fidelity Information Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer

TMLR 2023 POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer

NeurIPS 2023 PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning Neeratyoy Mallik, Edward Bergman, Carl Hvarfner, Danny Stoll, Maciej Janowski, Marius Lindauer, Luigi Nardi, Frank Hutter

AutoML 2023 Self-Adjusting Weighted Expected Improvement for Bayesian Optimization Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer

AutoML 2023 Symbolic Explanations for Hyperparameter Optimization Sarah Segel, Helena Graf, Alexander Tornede, Bernd Bischl, Marius Lindauer

ICLR 2022 $\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization Carl Hvarfner, Danny Stoll, Artur Souza, Marius Lindauer, Frank Hutter, Luigi Nardi

JMLR 2022 Auto-Sklearn 2.0: Hands-Free AutoML via Meta-Learning Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter

JAIR 2022 Automated Dynamic Algorithm Configuration Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter

JAIR 2022 Automated Reinforcement Learning (AutoRL): A Survey and Open Problems 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

ECML-PKDD 2022 Efficient Automated Deep Learning for Time Series Forecasting Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer

NeurIPSW 2022 PriorBand: HyperBand + Human Expert Knowledge Neeratyoy Mallik, Carl Hvarfner, Danny Stoll, Maciej Janowski, Eddie Bergman, Marius Lindauer, Luigi Nardi, Frank Hutter

MLOSS 2022 SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter

NeurIPSW 2022 Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis Carolin Benjamins, Anja Jankovic, Elena Raponi, Koen van der Blom, Marius Lindauer, Carola Doerr

ICMLW 2021 Bag of Baselines for Multi-Objective Joint Neural Architecture Search and Hyperparameter Optimization Sergio Izquierdo, Julia Guerrero-Viu, Sven Hauns, Guilherme Miotto, Simon Schrodi, André Biedenkapp, Thomas Elsken, Difan Deng, Marius Lindauer, Frank Hutter

ECML-PKDD 2021 Bayesian Optimization with a Prior for the Optimum Artur L. F. Souza, Luigi Nardi, Leonardo B. Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter

IJCAI 2021 DACBench: A Benchmark Library for Dynamic Algorithm Configuration Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer

NeurIPS 2021 Explaining Hyperparameter Optimization via Partial Dependence Plots Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl

ICML 2021 Self-Paced Context Evaluation for Contextual Reinforcement Learning Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer

ICML 2021 TempoRL: Learning When to Act André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer

ICMLW 2021 Towards Explaining Hyperparameter Optimization via Partial Dependence Plots Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl

NeurIPS 2021 Well-Tuned Simple Nets Excel on Tabular Datasets Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka

JMLR 2020 Best Practices for Scientific Research on Neural Architecture Search Marius Lindauer, Frank Hutter

IJCAI 2019 An Evolution Strategy with Progressive Episode Lengths for Playing Games Lior Fuks, Noor H. Awad, Frank Hutter, Marius Lindauer

JAIR 2019 Pitfalls and Best Practices in Algorithm Configuration Katharina Eggensperger, Marius Lindauer, Frank Hutter

MLJ 2018 Efficient Benchmarking of Algorithm Configurators via Model-Based Surrogates Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown

IJCAI 2018 Neural Networks for Predicting Algorithm Runtime Distributions Katharina Eggensperger, Marius Lindauer, Frank Hutter

AAAI 2018 Warmstarting of Model-Based Algorithm Configuration Marius Lindauer, Frank Hutter

IJCAI 2017 AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract) Marius Lindauer, Frank Hutter, Holger H. Hoos, Torsten Schaub

AAAI 2017 Efficient Parameter Importance Analysis via Ablation with Surrogates Andre Biedenkapp, Marius Lindauer, Katharina Eggensperger, Frank Hutter, Chris Fawcett, Holger H. Hoos

JAIR 2015 AutoFolio: An Automatically Configured Algorithm Selector Marius Lindauer, Holger H. Hoos, Frank Hutter, Torsten Schaub