Aaron Klein

According to our database, Aaron Klein authored at least 35 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

Warmstarting for Scaling Language Models.

Neeratyoy Mallik, Maciej Janowski, Johannes Hog, Herilalaina Rakotoarison, Aaron Klein, Josif Grabocka, Frank Hutter

CoRR, 2024

Hyperparameter Optimization in Machine Learning.

Luca Franceschi, Michele Donini, Valerio Perrone, Aaron Klein, Cédric Archambeau, Matthias W. Seeger, Massimiliano Pontil, Paolo Frasconi

CoRR, 2024

LLM Compression with Neural Architecture Search.

Rhea Sanjay Sukthanker, Benedikt Staffler, Frank Hutter, Aaron Klein

CoRR, 2024

Structural Pruning of Pre-trained Language Models via Neural Architecture Search.

Aaron Klein, Jacek Golebiowski, Xingchen Ma, Valerio Perrone, Cédric Archambeau

CoRR, 2024

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, Masayuki Yano

SIAM/ASA J. Uncertain. Quantification, September, 2023

Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation.

Sigrid Passano Hellan, Huibin Shen, François-Xavier Aubet, David Salinas, Aaron Klein

CoRR, 2023

Optimizing Hyperparameters with Conformal Quantile Regression.

David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau

Proceedings of the International Conference on Machine Learning, 2023

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

Proceedings of the International Conference on Automated Machine Learning, 2022

Automatic Termination for Hyperparameter Optimization.

Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau

Proceedings of the International Conference on Automated Machine Learning, 2022

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

Overfitting in Bayesian Optimization: an empirical study and early-stopping solution.

Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau

CoRR, 2021

HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO.

Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor H. Awad, Marius Lindauer, Frank Hutter

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

BORE: Bayesian Optimization by Density-Ratio Estimation.

Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos

Proceedings of the 38th International Conference on Machine Learning, 2021

Hyperparameter Transfer Learning with Adaptive Complexity.

Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Efficient bayesian hyperparameter optimization

Aaron Klein

PhD thesis, 2020

Model-based Asynchronous Hyperparameter Optimization.

Louis C. Tiao, Aaron Klein, Cédric Archambeau, Matthias W. Seeger

CoRR, 2020

2019

Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings.

Matilde Gargiani, Aaron Klein, Stefan Falkner, Frank Hutter

CoRR, 2019

Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization.

Aaron Klein, Frank Hutter

CoRR, 2019

Meta-Surrogate Benchmarking for Hyperparameter Optimization.

Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier González

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

NAS-Bench-101: Towards Reproducible Neural Architecture Search.

Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, Frank Hutter

Proceedings of the 36th International Conference on 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

Proceedings of the Automated Machine Learning - Methods, Systems, Challenges, 2019

Auto-sklearn: Efficient and Robust Automated Machine Learning.

Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter

Proceedings of the Automated Machine Learning - Methods, Systems, Challenges, 2019

2018

Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search.

Arber Zela, Aaron Klein, Stefan Falkner, Frank Hutter

CoRR, 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

BOHB: Robust and Efficient Hyperparameter Optimization at Scale.

Stefan Falkner, Aaron Klein, Frank Hutter

Proceedings of the 35th International Conference on Machine Learning, 2018

Practical Hyperparameter Optimization for Deep Learning.

Stefan Falkner, Aaron Klein, Frank Hutter

Proceedings of the 6th International Conference on Learning Representations, 2018

Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow.

Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox

Proceedings of the Computer Vision - ECCV 2018, 2018

2017

The Sacred Infrastructure for Computational Research.

Klaus Greff, Aaron Klein, Martin Chovanec, Frank Hutter, Jürgen Schmidhuber

Proceedings of the 16th Python in Science Conference 2017, 2017

Learning Curve Prediction with Bayesian Neural Networks.

Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, Frank Hutter

Proceedings of the 5th International Conference on Learning Representations, 2017

Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets.

Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Asynchronous Stochastic Gradient MCMC with Elastic Coupling.

Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter

CoRR, 2016

Bayesian Optimization with Robust Bayesian Neural Networks.

Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Towards Automatically-Tuned Neural Networks.

Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Frank Hutter

Proceedings of the 2016 Workshop on Automatic Machine Learning, 2016

2015

Efficient and Robust Automated Machine Learning.

Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015