Efficient and Robust Automated Machine Learning. - Matthias Feurer; Aaron Klein; Katharina Eggensperger; Jost Tobias Springenberg; Manuel Blum 002; Frank Hutter (NIPS, 2015)

Towards Automatically-Tuned Neural Networks. - Hector Mendoza; Aaron Klein; Matthias Feurer; Jost Tobias Springenberg; Frank Hutter (AutoML@ICML, 2016)

Bayesian Optimization with Robust Bayesian Neural Networks. - Jost Tobias Springenberg; Aaron Klein; Stefan Falkner; Frank Hutter (NIPS, 2016)

Asynchronous Stochastic Gradient MCMC with Elastic Coupling. - Jost Tobias Springenberg; Aaron Klein; Stefan Falkner; Frank Hutter (CoRR, 2016)

Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. - Aaron Klein; Stefan Falkner; Simon Bartels; Philipp Hennig; Frank Hutter (AISTATS, 2017)

Learning Curve Prediction with Bayesian Neural Networks. - Aaron Klein; Stefan Falkner; Jost Tobias Springenberg; Frank Hutter (ICLR (Poster), 2017)

The Sacred Infrastructure for Computational Research. - Klaus Greff; Aaron Klein; Martin Chovanec; Frank Hutter; Jürgen Schmidhuber (SciPy, 2017)

Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow. - Eddy Ilg; Özgün Çiçek; Silvio Galesso; Aaron Klein; Osama Makansi; Frank Hutter; Thomas Brox (ECCV (7), 2018)

Practical Hyperparameter Optimization for Deep Learning. - Stefan Falkner; Aaron Klein; Frank Hutter (ICLR (Workshop), 2018)

BOHB: Robust and Efficient Hyperparameter Optimization at Scale. - Stefan Falkner; Aaron Klein; Frank Hutter (ICML, 2018)

Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks. - Eddy Ilg; Özgün Çiçek; Silvio Galesso; Aaron Klein; Osama Makansi; Frank Hutter; Thomas Brox (CoRR, 2018)

Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search. - Arber Zela; Aaron Klein; Stefan Falkner; Frank Hutter (CoRR, 2018)

Auto-sklearn: Efficient and Robust Automated Machine Learning. - Matthias Feurer; Aaron Klein; Katharina Eggensperger; Jost Tobias Springenberg; Manuel Blum 002; Frank Hutter (Automated Machine Learning, 2019)

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)

NAS-Bench-101: Towards Reproducible Neural Architecture Search. - Chris Ying; Aaron Klein; Eric Christiansen; Esteban Real; Kevin Murphy 002; Frank Hutter (ICML, 2019)

Meta-Surrogate Benchmarking for Hyperparameter Optimization. - Aaron Klein; Zhenwen Dai; Frank Hutter; Neil D. Lawrence; Javier González 002 (NeurIPS, 2019)

Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization. - Aaron Klein; Frank Hutter (CoRR, 2019)

Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings. - Matilde Gargiani; Aaron Klein; Stefan Falkner; Frank Hutter (CoRR, 2019)

Model-based Asynchronous Hyperparameter Optimization. - Louis C. Tiao; Aaron Klein; Cédric Archambeau; Matthias W. Seeger (CoRR, 2020)

Efficient bayesian hyperparameter optimization - Aaron Klein (2020)

Hyperparameter Transfer Learning with Adaptive Complexity. - Samuel Horváth; Aaron Klein; Peter Richtárik; Cédric Archambeau (AISTATS, 2021)

BORE: Bayesian Optimization by Density-Ratio Estimation. - Louis C. Tiao; Aaron Klein; Matthias W. Seeger; Edwin V. Bonilla; Cédric Archambeau; Fabio Ramos 001 (ICML, 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)

Overfitting in Bayesian Optimization: an empirical study and early-stopping solution. - Anastasia Makarova; Huibin Shen; Valerio Perrone; Aaron Klein; Jean Baptiste Faddoul; Andreas Krause 001; Matthias W. Seeger; Cédric Archambeau (CoRR, 2021)

Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints. - Jan Sosulski; David Hübner; Aaron Klein; Michael Tangermann (CoRR, 2021)

Automatic Termination for Hyperparameter Optimization. - Anastasia Makarova; Huibin Shen; Valerio Perrone; Aaron Klein; Jean Baptiste Faddoul; Andreas Krause 001; Matthias W. Seeger; Cédric Archambeau (AutoML, 2022)

Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. - David Salinas; Matthias W. Seeger; Aaron Klein; Valerio Perrone; Martin Wistuba; Cédric Archambeau (AutoML, 2022)

Optimizing Hyperparameters with Conformal Quantile Regression. - David Salinas; Jacek Golebiowski; Aaron Klein; Matthias W. Seeger; Cédric Archambeau (ICML, 2023)

Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation. - Sigrid Passano Hellan; Huibin Shen; François-Xavier Aubet; David Salinas; Aaron Klein (CoRR, 2023)

Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov-Galerkin Method. - Christophe Audouze; Aaron Klein; Adrian Butscher; Nigel J. W. Morris; Prasanth Nair 001; Masayuki Yano (SIAM/ASA J. Uncertain. Quantification, 2023)

HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models. - Rhea Sukthanker; Arber Zela; Benedikt Staffler; Aaron Klein; Lennart Purucker; Jörg K. H. Franke; Frank Hutter (NeurIPS, 2024)

LLM Compression with Neural Architecture Search. - Rhea Sanjay Sukthanker; Benedikt Staffler; Frank Hutter; Aaron Klein (CoRR, 2024)

Hyperparameter Optimization in Machine Learning. - Luca Franceschi 001; Michele Donini; Valerio Perrone; Aaron Klein; Cédric Archambeau; Matthias W. Seeger; Massimiliano Pontil; Paolo Frasconi (CoRR, 2024)

Warmstarting for Scaling Language Models. - Neeratyoy Mallik; Maciej Janowski; Johannes Hog; Herilalaina Rakotoarison; Aaron Klein; Josif Grabocka; Frank Hutter (CoRR, 2024)

Hyperband-based Bayesian Optimization for Black-box Prompt Selection. - Lennart Schneider; Martin Wistuba; Aaron Klein; Jacek Golebiowski; Giovanni Zappella; Felice Antonio Merra (CoRR, 2024)

Structural Pruning of Pre-trained Language Models via Neural Architecture Search. - Aaron Klein; Jacek Golebiowski; Xingchen Ma; Valerio Perrone; Cédric Archambeau (Trans. Mach. Learn. Res., 2024)

Improving LLM-based Global Optimization with Search Space Partitioning. - Andrej Schwanke; Lyubomir Ivanov; David Salinas; Fabio Ferreira; Aaron Klein; Frank Hutter; Arber Zela (CoRR, 2025)