Klein, Aaron

22 publications

ICML 2025 Hyperband-Based Bayesian Optimization for Black-Box Prompt Selection Lennart Schneider, Martin Wistuba, Aaron Klein, Jacek Golebiowski, Giovanni Zappella, Felice Antonio Merra

FnTML 2025 Hyperparameter Optimization in Machine Learning Luca Franceschi, Michele Donini, Valerio Perrone, Aaron Klein, Cédric Archambeau, Matthias W. Seeger, Massimiliano Pontil, Paolo Frasconi

AutoML 2025 Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimization Sigrid Passano Hellan, Huibin Shen, Francois-Xavier Aubet, David Salinas, Aaron Klein

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

NeurIPSW 2024 Large Language Model Compression with Neural Architecture Search Rhea Sanjay Sukthanker, Benedikt Staffler, Frank Hutter, Aaron Klein

TMLR 2024 Structural Pruning of Pre-Trained Language Models via Neural Architecture Search Aaron Klein, Jacek Golebiowski, Xingchen Ma, Valerio Perrone, Cedric Archambeau

NeurIPSW 2024 Warmstarting for Scaling Language Models Neeratyoy Mallik, Maciej Janowski, Johannes Hog, Herilalaina Rakotoarison, Aaron Klein, Josif Grabocka, Frank Hutter

ICML 2023 Optimizing Hyperparameters with Conformal Quantile Regression David Salinas, Jacek Golebiowski, Aaron Klein, Matthias Seeger, Cedric Archambeau

AutoML 2022 Automatic Termination for Hyperparameter Optimization Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias Seeger, Cedric Archambeau

AutoML 2022 Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research David Salinas, Matthias Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cedric Archambeau

AISTATS 2021 Hyperparameter Transfer Learning with Adaptive Complexity Samuel Horváth, Aaron Klein, Peter Richtarik, Cedric Archambeau

ICML 2021 BORE: Bayesian Optimization by Density-Ratio Estimation Louis C Tiao, Aaron Klein, Matthias W Seeger, Edwin V. Bonilla, Cedric Archambeau, Fabio Ramos

ICMLW 2021 Dynamic Pruning of a Neural Network via Gradient Signal-to-Noise Ratio Julien Niklas Siems, Aaron Klein, Cedric Archambeau, Maren Mahsereci

NeurIPS 2019 Meta-Surrogate Benchmarking for Hyperparameter Optimization Aaron Klein, Zhenwen Dai, Frank Hutter, Neil Lawrence, Javier Gonzalez

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

ICML 2018 BOHB: Robust and Efficient Hyperparameter Optimization at Scale Stefan Falkner, Aaron Klein, Frank Hutter

ECCV 2018 Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow Eddy Ilg, Ozgun Cicek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox

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

ICLR 2017 Learning Curve Prediction with Bayesian Neural Networks Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, Frank Hutter

NeurIPS 2016 Bayesian Optimization with Robust Bayesian Neural Networks Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter

AutoML 2016 Towards Automatically-Tuned Neural Networks Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Frank Hutter

NeurIPS 2015 Efficient and Robust Automated Machine Learning Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, Frank Hutter