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Marius Lindauer

24 papers · 2017–2026 · 7 venues · across top CS/AI venues

Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.

Achievements

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+9 more ↓ πŸ—ΊοΈ Taxonomy Completionist (35) 🌍 Conference Polyglot Β· Silver πŸƒ Academic Marathon Β· Sprint πŸŒ‰ Interdisciplinary Bridge Β· Silver 🐣 Hot Topic Early Bird
🀝 Dynamic Duo (14) πŸ† Keyword Champion (3) πŸ† Grand Slam πŸ”¬ Deep Specialist (18) 🌈 Renaissance Researcher (5) πŸ”₯ Unstoppable Β· Silver ⚑ Prolific Year Β· Productive πŸ’Ž Century Club Β· Bronze πŸ—ƒοΈ Keyword Collector (80)

Venues

AutoML (6) ICML (4) IJCAI (4) JMLR (4) NeurIPS (3) AAAI (2) ICLR (1)

Top co-authors

Frank Hutter (14) Theresa Eimer (4) Alexander Tornede (4) AndrΓ© Biedenkapp (4) Katharina Eggensperger (3) Matthias Feurer (3) Bernd Bischl (3) Carolin Benjamins (3) Edward Bergman (2) Luigi Nardi (2)

Research topics

Machine Learning (22) Optimization & Theory (20) Artificial Intelligence (9) Core AI (7) Application Areas (5) Reinforcement Learning (4) Learning Types (4) Deep Learning (3) Mathematics & Optimization (3) Learning Paradigms Β· Artificial Intelligence (3) Optimization (3) Applications (2) Core Methods (2) Learning Paradigms Β· Machine Learning (2) Methods (2)

Keywords

hyperparameter optimization (17) bayesian optimization (7) neural architecture search (3) surrogate model (3) algorithm selection (3) reinforcement learning (2) acquisition function (2) sample efficiency (2) algorithm configuration (2) deep reinforcement learning (2) automated machine learning (2) game theory (1) policy learning (1) deep learning (1) model architecture (1) black-box optimization (1) interpretable machine learning (1) game playing (1) preference learning (1) model complexity (1)

Papers

HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization AAAI 2026 Auto-nnU-Net: Towards Automated Medical Image Segmentation AutoML 2025 DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning JMLR 2025 Revisiting Learning Rate Control AutoML 2025 Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning AAAI 2024 Position: A Call to Action for a Human-Centered AutoML Paradigm ICML 2024 AutoRL Hyperparameter Landscapes AutoML 2023 Hyperparameters in Reinforcement Learning and How To Tune Them ICML 2023 Learning Activation Functions for Sparse Neural Networks AutoML 2023 PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning NeurIPS 2023 Self-Adjusting Weighted Expected Improvement for Bayesian Optimization AutoML 2023 Symbolic Explanations for Hyperparameter Optimization AutoML 2023 Ο€BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization ICLR 2022 Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning JMLR 2022 SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization JMLR 2022 DACBench: A Benchmark Library for Dynamic Algorithm Configuration IJCAI 2021 Explaining Hyperparameter Optimization via Partial Dependence Plots NeurIPS 2021 Self-Paced Context Evaluation for Contextual Reinforcement Learning ICML 2021 TempoRL: Learning When to Act ICML 2021 Well-tuned Simple Nets Excel on Tabular Datasets NeurIPS 2021 Best Practices for Scientific Research on Neural Architecture Search JMLR 2020 An Evolution Strategy with Progressive Episode Lengths for Playing Games IJCAI 2019 Neural Networks for Predicting Algorithm Runtime Distributions IJCAI 2018 AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract) IJCAI 2017