A Portfolio Solver for Answer Set Programming: Preliminary Report. - Martin Gebser; Roland Kaminski; Benjamin Kaufmann; Torsten Schaub; Marius Thomas Schneider; Stefan Ziller (LPNMR, 2011)

Potassco: The Potsdam Answer Set Solving Collection. - Martin Gebser; Benjamin Kaufmann; Roland Kaminski; Max Ostrowski; Torsten Schaub; Marius Schneider 001 (AI Commun., 2011)

Centurio, a General Game Player: Parallel, Java- and ASP-based. - Maximilian Möller; Marius Schneider 001; Martin Wegner; Torsten Schaub (Künstliche Intell., 2011)

aspeed: ASP-based Solver Scheduling. - Holger H. Hoos; Roland Kaminski; Torsten Schaub; Marius Schneider 001 (ICLP (Technical Communications), 2012)

Surviving Solver Sensitivity: An ASP Practitioner's Guide. - Bryan Silverthorn; Yuliya Lierler; Marius Schneider 001 (ICLP (Technical Communications), 2012)

Quantifying Homogeneity of Instance Sets for Algorithm Configuration. - Marius Schneider 001; Holger H. Hoos (LION, 2012)

Robust Benchmark Set Selection for Boolean Constraint Solvers. - Holger H. Hoos; Benjamin Kaufmann; Torsten Schaub; Marius Schneider 001 (LION, 2013)

Ricochet Robots: A Transverse ASP Benchmark. - Martin Gebser; Holger Jost; Roland Kaminski; Philipp Obermeier; Orkunt Sabuncu; Torsten Schaub; Marius Schneider 001 (LPNMR, 2013)

AClib: A Benchmark Library for Algorithm Configuration. - Frank Hutter; Manuel López-Ibáñez 001; Chris Fawcett; Marius Lindauer; Holger H. Hoos; Kevin Leyton-Brown; Thomas Stützle (LION, 2014)

Solver Scheduling via Answer Set Programming. - Holger H. Hoos; Roland Kaminski; Marius Lindauer; Torsten Schaub (CoRR, 2014)

claspfolio 2: Advances in Algorithm Selection for Answer Set Programming. - Holger H. Hoos; Marius Lindauer; Torsten Schaub (Theory Pract. Log. Program., 2014)

AutoFolio: Algorithm Configuration for Algorithm Selection. - Marius Lindauer; Holger H. Hoos; Frank Hutter; Torsten Schaub (AAAI Workshop: Algorithm Configuration, 2015)

From Sequential Algorithm Selection to Parallel Portfolio Selection. - Marius Lindauer; Holger H. Hoos; Frank Hutter (LION, 2015)

SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers. - Stefan Falkner; Marius Lindauer; Frank Hutter (SAT, 2015)

Reports from the 2015 AAAI Workshop Program. - 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; et al. (AI Mag., 2015)

AutoFolio: An Automatically Configured Algorithm Selector. - Marius Lindauer; Holger H. Hoos; Frank Hutter; Torsten Schaub (J. Artif. Intell. Res., 2015)

aspeed: Solver scheduling via answer set programming. - Holger H. Hoos; Roland Kaminski; Marius Lindauer; Torsten Schaub (Theory Pract. Log. Program., 2015)

Algorithm selection, scheduling and configuration of Boolean constraint solvers - Marius Lindauer (2015)

An Empirical Study of Per-instance Algorithm Scheduling. - Marius Lindauer; Rolf-David Bergdoll; Frank Hutter (LION, 2016)

SpyBug: Automated Bug Detection in the Configuration Space of SAT Solvers. - Norbert Manthey; Marius Lindauer (SAT, 2016)

ASlib: A benchmark library for algorithm selection. - Bernd Bischl; Pascal Kerschke; Lars Kotthoff; Marius Lindauer; Yuri Malitsky; Alexandre Fréchette; Holger H. Hoos; Frank Hutter; Kevin Leyton-Brown; Kevin Tierney; Joaquin Vanschoren (Artif. Intell., 2016)

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

Improving local search in a minimum vertex cover solver for classes of networks. - Markus Wagner 007; Tobias Friedrich 001; Marius Lindauer (CEC, 2017)

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

Open Algorithm Selection Challenge 2017: Setup and Scenarios. - Marius Lindauer; Jan N. van Rijn; Lars Kotthoff (OASC, 2017)

The Configurable SAT Solver Challenge (CSSC). - Frank Hutter; Marius Lindauer; Adrian Balint; Sam Bayless; Holger H. Hoos; Kevin Leyton-Brown (Artif. Intell., 2017)

Automatic construction of parallel portfolios via algorithm configuration. - Marius Lindauer; Holger H. Hoos; Kevin Leyton-Brown; Torsten Schaub (Artif. Intell., 2017)

Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates. - Katharina Eggensperger; Marius Lindauer; Holger H. Hoos; Frank Hutter; Kevin Leyton-Brown (CoRR, 2017)

Predicting Runtime Distributions using Deep Neural Networks. - Katharina Eggensperger; Marius Lindauer; Frank Hutter (CoRR, 2017)

Selection and Configuration of Parallel Portfolios. - Marius Lindauer; Holger H. Hoos; Frank Hutter; Kevin Leyton-Brown (Handbook of Parallel Constraint Reasoning, 2018)

Warmstarting of Model-Based Algorithm Configuration. - Marius Lindauer; Frank Hutter (AAAI, 2018)

Neural Networks for Predicting Algorithm Runtime Distributions. - Katharina Eggensperger; Marius Lindauer; Frank Hutter (IJCAI, 2018)

CAVE: Configuration Assessment, Visualization and Evaluation. - Andre Biedenkapp; Joshua Marben; Marius Lindauer; Frank Hutter (LION, 2018)

The Algorithm Selection Competition Series 2015-17. - Marius Lindauer; Jan N. van Rijn; Lars Kotthoff (CoRR, 2018)

A case study of algorithm selection for the traveling thief problem. - Markus Wagner 007; Marius Lindauer; Mustafa Misir; Samadhi Nallaperuma; Frank Hutter (J. Heuristics, 2018)

Efficient benchmarking of algorithm configurators via model-based surrogates. - Katharina Eggensperger; Marius Lindauer; Holger H. Hoos; Frank Hutter; Kevin Leyton-Brown (Mach. Learn., 2018)

Towards Automatically-Tuned Deep Neural Networks. - Hector Mendoza; Aaron Klein; Matthias Feurer; Jost Tobias Springenberg; Matthias Urban; Michael Burkart; Maximilian Dippel; Marius Lindauer; Frank Hutter (Automated Machine Learning, 2019)

Automated Algorithm Selection - Predict which algorithm to use! - Marius Lindauer (AMIR@ECIR, 2019)

Hands-On Automated Machine Learning Tools: Auto-Sklearn and Auto-PyTorch. - Marius Lindauer (AMIR@ECIR, 2019)

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

The algorithm selection competitions 2015 and 2017. - Marius Lindauer; Jan N. van Rijn; Lars Kotthoff (Artif. Intell., 2019)

Towards White-box Benchmarks for Algorithm Control. - André Biedenkapp; H. Furkan Bozkurt; Frank Hutter; Marius Lindauer (CoRR, 2019)

Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters. - Marius Lindauer; Matthias Feurer; Katharina Eggensperger; André Biedenkapp; Frank Hutter (CoRR, 2019)

BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters. - Marius Lindauer; Katharina Eggensperger; Matthias Feurer; André Biedenkapp; Joshua Marben; Philipp Müller 008; Frank Hutter (CoRR, 2019)

Best Practices for Scientific Research on Neural Architecture Search. - Marius Lindauer; Frank Hutter (CoRR, 2019)

Pitfalls and Best Practices in Algorithm Configuration. - Katharina Eggensperger; Marius Lindauer; Frank Hutter (J. Artif. Intell. Res., 2019)

Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework. - André Biedenkapp; H. Furkan Bozkurt; Theresa Eimer; Frank Hutter; Marius Lindauer (ECAI, 2020)

Learning Step-Size Adaptation in CMA-ES. - Gresa Shala; André Biedenkapp; Noor H. Awad; Steven Adriaensen; Marius Lindauer; Frank Hutter (PPSN (1), 2020)

Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. - Lucas Zimmer; Marius Lindauer; Frank Hutter (CoRR, 2020)

Prior-guided Bayesian Optimization. - Artur L. F. Souza; Luigi Nardi; Leonardo B. Oliveira; Kunle Olukotun; Marius Lindauer; Frank Hutter (CoRR, 2020)

Auto-Sklearn 2.0: The Next Generation. - Matthias Feurer; Katharina Eggensperger; Stefan Falkner; Marius Lindauer; Frank Hutter (CoRR, 2020)

Neural Model-based Optimization with Right-Censored Observations. - Katharina Eggensperger; Kai Haase; Philipp Müller 008; Marius Lindauer; Frank Hutter (CoRR, 2020)

Squirrel: A Switching Hyperparameter Optimizer. - Noor H. Awad; Gresa Shala; Difan Deng; Neeratyoy Mallik; Matthias Feurer; Katharina Eggensperger; André Biedenkapp; Diederick Vermetten; Hao Wang 025; Carola Doerr; Marius Lindauer; Frank Hutter (CoRR, 2020)

Learning Heuristic Selection with Dynamic Algorithm Configuration. - David Speck 001; André Biedenkapp; Frank Hutter; Robert Mattmüller; Marius Lindauer (ICAPS, 2021)

TempoRL: Learning When to Act. - André Biedenkapp; Raghu Rajan; Frank Hutter; Marius Lindauer (ICML, 2021)

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

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

HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. - Katharina Eggensperger; Philipp Müller 008; Neeratyoy Mallik; Matthias Feurer; René Sass; Aaron Klein; Noor H. Awad; Marius Lindauer; Frank Hutter (NeurIPS Datasets and Benchmarks, 2021)

Well-tuned Simple Nets Excel on Tabular Datasets. - Arlind Kadra; Marius Lindauer; Frank Hutter; Josif Grabocka (NeurIPS, 2021)

Explaining Hyperparameter Optimization via Partial Dependence Plots. - Julia Moosbauer; Julia Herbinger; Giuseppe Casalicchio; Marius Lindauer; Bernd Bischl (NeurIPS, 2021)

Bayesian Optimization with a Prior for the Optimum. - Artur L. F. Souza; Luigi Nardi; Leonardo B. Oliveira; Kunle Olukotun; Marius Lindauer; Frank Hutter (ECML/PKDD (3), 2021)

Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization. - Julia Guerrero-Viu; Sven Hauns; Sergio Izquierdo; Guilherme Miotto; Simon Schrodi; Andre Biedenkapp; Thomas Elsken; Difan Deng; Marius Lindauer; Frank Hutter (CoRR, 2021)

Automatic Risk Adaptation in Distributional Reinforcement Learning. - Frederik Schubert; Theresa Eimer; Bodo Rosenhahn; Marius Lindauer (CoRR, 2021)

Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data. - Arlind Kadra; Marius Lindauer; Frank Hutter; Josif Grabocka (CoRR, 2021)

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. - Marius Lindauer; Katharina Eggensperger; Matthias Feurer; André Biedenkapp; Difan Deng; Carolin Benjamins; René Sass; Frank Hutter (CoRR, 2021)

CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. - Carolin Benjamins; Theresa Eimer; Frederik Schubert; André Biedenkapp; Bodo Rosenhahn; Frank Hutter; Marius Lindauer (CoRR, 2021)

Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019. - Zhengying Liu; Adrien Pavao; Zhen Xu 007; Sergio Escalera; Fabio Ferreira; Isabelle Guyon; Sirui Hong; Frank Hutter; Rongrong Ji; Júlio C. S. Jacques Júnior; Ge Li; Marius Lindauer; et al. (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. - Lucas Zimmer; Marius Lindauer; Frank Hutter (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Searching in the Forest for Local Bayesian Optimization. - Difan Deng; Marius Lindauer (Meta-Knowledge Transfer @ ECML/PKDD, 2022)

$ pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. - Carl Hvarfner; Danny Stoll; Artur L. F. Souza; Marius Lindauer; Frank Hutter; Luigi Nardi (ICLR, 2022)

Efficient Automated Deep Learning for Time Series Forecasting. - Difan Deng; Florian Karl; Frank Hutter; Bernd Bischl; Marius Lindauer (ECML/PKDD (3), 2022)

Developing Open Source Educational Resources for Machine Learning and Data Science. - Ludwig Bothmann; Sven Strickroth; Giuseppe Casalicchio; David Rügamer; Marius Lindauer; Fabian Scheipl; Bernd Bischl (Teaching ML, 2022)

Contextualize Me - The Case for Context in Reinforcement Learning. - Carolin Benjamins; Theresa Eimer; Frederik Schubert; Aditya Mohan; André Biedenkapp; Bodo Rosenhahn; Frank Hutter; Marius Lindauer (CoRR, 2022)

πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. - Carl Hvarfner; Danny Stoll; Artur L. F. Souza; Marius Lindauer; Frank Hutter; Luigi Nardi (CoRR, 2022)

Towards Meta-learned Algorithm Selection using Implicit Fidelity Information. - Aditya Mohan; Tim Ruhkopf; Marius Lindauer (CoRR, 2022)

DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning. - René Sass; Eddie Bergman; André Biedenkapp; Frank Hutter; Marius Lindauer (CoRR, 2022)

Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. - Julia Moosbauer; Giuseppe Casalicchio; Marius Lindauer; Bernd Bischl (CoRR, 2022)

PI is back! Switching Acquisition Functions in Bayesian Optimization. - Carolin Benjamins; Elena Raponi; Anja Jankovic 001; Koen van der Blom; Maria Laura Santoni; Marius Lindauer; Carola Doerr (CoRR, 2022)

Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. - Carolin Benjamins; Anja Jankovic 001; Elena Raponi; Koen van der Blom; Marius Lindauer; Carola Doerr (CoRR, 2022)

Hyperparameters in Contextual RL are Highly Situational. - Theresa Eimer; Carolin Benjamins; Marius Lindauer (CoRR, 2022)

Automated Dynamic Algorithm Configuration. - Steven Adriaensen; André Biedenkapp; Gresa Shala; Noor H. Awad; Theresa Eimer; Marius Lindauer; Frank Hutter (J. Artif. Intell. Res., 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 (J. Artif. Intell. Res., 2022)

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning. - Matthias Feurer; Katharina Eggensperger; Stefan Falkner; Marius Lindauer; Frank Hutter (J. Mach. Learn. Res., 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 (J. Mach. Learn. Res., 2022)

Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. - Carolin Benjamins; Elena Raponi; Anja Jankovic 001; Carola Doerr; Marius Lindauer (AutoML, 2023)

Learning Activation Functions for Sparse Neural Networks. - Mohammad Loni; Aditya Mohan; Mehdi Asadi; Marius Lindauer (AutoML, 2023)

AutoRL Hyperparameter Landscapes. - Aditya Mohan; Carolin Benjamins; Konrad Wienecke; Alexander Dockhorn; Marius Lindauer (AutoML, 2023)

Symbolic Explanations for Hyperparameter Optimization. - Sarah Segel; Helena Graf; Alexander Tornede; Bernd Bischl; Marius Lindauer (AutoML, 2023)

Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. - Carolin Benjamins; Elena Raponi; Anja Jankovic 001; Carola Doerr; Marius Lindauer (GECCO Companion, 2023)

Hyperparameters in Reinforcement Learning and How To Tune Them. - Theresa Eimer; Marius Lindauer; Roberta Raileanu (ICML, 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 (NeurIPS, 2023)

Automated Machine Learning for Remaining Useful Life Predictions. - Marc-André Zöller; Fabian Mauthe; Peter Zeiler; Marius Lindauer; Marco F. Huber (SMC, 2023)

Structure in Reinforcement Learning: A Survey and Open Problems. - Aditya Mohan; Amy Zhang 001; Marius Lindauer (CoRR, 2023)

Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332). - Diederick Vermetten; Martin S. Krejca; Marius Lindauer; Manuel López-Ibáñez 001; Katherine M. Malan (Dagstuhl Reports, 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 (Trans. Mach. Learn. Res., 2023)

MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. - Tim Ruhkopf; Aditya Mohan; Difan Deng; Alexander Tornede; Frank Hutter; Marius Lindauer (Trans. Mach. Learn. Res., 2023)

POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. - Frederik Schubert; Carolin Benjamins; Sebastian Döhler; Bodo Rosenhahn; Marius Lindauer (Trans. Mach. Learn. Res., 2023)

Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. - 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 (WIREs Data. Mining. Knowl. Discov., 2023)

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

Hyperparameter Importance Analysis for Multi-Objective AutoML. - Daphne Theodorakopoulos; Frederic T. Stahl; Marius Lindauer (ECAI, 2024)

Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. - Carolin Benjamins; Gjorgjina Cenikj; Ana Nikolikj; Aditya Mohan; Tome Eftimov; Marius Lindauer (GECCO Companion, 2024)

Position: A Call to Action for a Human-Centered AutoML Paradigm. - Marius Lindauer; Florian Karl; Anne Klier; Julia Moosbauer; Alexander Tornede; Andreas Müller 024; Frank Hutter; Matthias Feurer; Bernd Bischl (ICML, 2024)

Auto-sktime: Automated Time Series Forecasting. - Marc-André Zöller; Marius Lindauer; Marco F. Huber (LION, 2024)

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

Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach. - Difan Deng; Marius Lindauer (CoRR, 2024)

ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. - Jannis Becktepe; Julian Dierkes; Carolin Benjamins; Aditya Mohan; David Salinas; Raghu Rajan; Frank Hutter; Holger H. Hoos; Marius Lindauer; Theresa Eimer (CoRR, 2024)

Automated Machine Learning For Computational Mechanics (Dagstuhl Seminar 24282). - Elena Raponi; Lars Kotthoff; Hyunsun Alicia Kim; Marius Lindauer (Dagstuhl Reports, 2024)

Structure in Deep Reinforcement Learning: A Survey and Open Problems. - Aditya Mohan; Amy Zhang 001; Marius Lindauer (J. Artif. Intell. Res., 2024)

AMLTK: A Modular AutoML Toolkit in Python. - Edward Bergman; Matthias Feurer; Aron Bahram; Amir Rezaei Balef; Lennart Purucker; Sarah Segel; Marius Lindauer; Frank Hutter; Katharina Eggensperger (J. Open Source Softw., 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 (Trans. Mach. Learn. Res., 2024)

AutoML in heavily constrained applications. - Felix Neutatz; Marius Lindauer; Ziawasch Abedjan (VLDB J., 2024)

How Green is AutoML for Tabular Data? - Felix Neutatz; Marius Lindauer; Ziawasch Abedjan (EDBT, 2025)

SynthACticBench: A Capability-Based Synthetic Benchmark for Algorithm Configuration. - Valentin Margraf; Anna Lappe; Marcel Wever; Carolin Benjamins; Eyke Hüllermeier; Marius Lindauer (GECCO, 2025)

Leveraging AutoML for Sustainable Deep Learning: A Multi- Objective HPO Approach on Deep Shift Neural Networks. - Leona Hennig; Marius Lindauer (Trans. Mach. Learn. Res., 2025)

HyperSHAP: Shapley Values and Interactions for Hyperparameter Importance. - Marcel Wever; Maximilian Muschalik; Fabian Fumagalli; Marius Lindauer (CoRR, 2025)

Neural Attention Search. - Difan Deng; Marius Lindauer (CoRR, 2025)

AutoML for Multi-Class Anomaly Compensation of Sensor Drift. - Melanie Schaller; Mathis Kruse; Antonio Ortega; Marius Lindauer; Bodo Rosenhahn (CoRR, 2025)

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks. - Nick Kocher; Christian Wassermann; Leona Hennig; Jonas Seng; Holger H. Hoos; Kristian Kersting; Marius Lindauer; Matthias S. Müller (CoRR, 2025)

Auto-nnU-Net: Towards Automated Medical Image Segmentation. - Jannis Becktepe; Leona Hennig; Steffen Oeltze-Jafra; Marius Lindauer (CoRR, 2025)

carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks. - Carolin Benjamins; Helena Graf; Sarah Segel; Difan Deng; Tim Ruhkopf; Leona Hennig; Soham Basu; Neeratyoy Mallik; Edward Bergman; Deyao Chen; François Clément; Matthias Feurer; et al. (CoRR, 2025)

Growing with Experience: Growing Neural Networks in Deep Reinforcement Learning. - Lukas Fehring; Marius Lindauer; Theresa Eimer (CoRR, 2025)

Revisiting Learning Rate Control. - Micha Henheik; Theresa Eimer; Marius Lindauer (CoRR, 2025)