Aaron Klein

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 35

Top Topics

Parameter Optimization

Top Venues

Publications

Aaron Klein, Jacek Golebiowski, Xingchen Ma, Valerio Perrone, Cédric Archambeau
Structural Pruning of Pre-trained Language Models via Neural Architecture Search. CoRR (2024)
David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau
Optimizing Hyperparameters with Conformal Quantile Regression. CoRR (2023)
Christophe Audouze, Aaron Klein, Adrian Butscher, Nigel J. W. Morris, Prasanth Nair, Masayuki Yano
Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov-Galerkin Method. SIAM/ASA J. Uncertain. Quantification 11 (3) (2023)
David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau
Optimizing Hyperparameters with Conformal Quantile Regression. ICML (2023)
Sigrid Passano Hellan, Huibin Shen, François-Xavier Aubet, David Salinas, Aaron Klein
Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation. CoRR (2023)
David Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. AutoML (2022)
Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau
Automatic Termination for Hyperparameter Optimization. AutoML (2022)
Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau
Hyperparameter Transfer Learning with Adaptive Complexity. CoRR (2021)
Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor H. Awad, Marius Lindauer, Frank Hutter
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. CoRR (2021)
Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos
BORE: Bayesian Optimization by Density-Ratio Estimation. CoRR (2021)
Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos
BORE: Bayesian Optimization by Density-Ratio Estimation. ICML (2021)
Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau
Overfitting in Bayesian Optimization: an empirical study and early-stopping solution. CoRR (2021)
Jan Sosulski, David Hübner, Aaron Klein, Michael Tangermann
Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints. CoRR (2021)
Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau
Hyperparameter Transfer Learning with Adaptive Complexity. AISTATS (2021)
Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor H. Awad, Marius Lindauer, Frank Hutter
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. NeurIPS Datasets and Benchmarks (2021)
Louis C. Tiao, Aaron Klein, Cédric Archambeau, Matthias W. Seeger
Model-based Asynchronous Hyperparameter Optimization. CoRR (2020)
Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, Frank Hutter
NAS-Bench-101: Towards Reproducible Neural Architecture Search. ICML (2019)
Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier Gonzalez
Meta-Surrogate Benchmarking for Hyperparameter Optimization. NeurIPS (2019)
Aaron Klein, Frank Hutter
Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization. CoRR (2019)
Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier Gonzalez
Meta-Surrogate Benchmarking for Hyperparameter Optimization. CoRR (2019)
Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, Frank Hutter
NAS-Bench-101: Towards Reproducible Neural Architecture Search. CoRR (2019)
Matilde Gargiani, Aaron Klein, Stefan Falkner, Frank Hutter
Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings. CoRR (2019)
Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow. ECCV (7) (2018)
Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks. CoRR (2018)
Stefan Falkner, Aaron Klein, Frank Hutter
BOHB: Robust and Efficient Hyperparameter Optimization at Scale. ICML (2018)
Arber Zela, Aaron Klein, Stefan Falkner, Frank Hutter
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search. CoRR (2018)
Stefan Falkner, Aaron Klein, Frank Hutter
Practical Hyperparameter Optimization for Deep Learning. ICLR (Workshop) (2018)
Stefan Falkner, Aaron Klein, Frank Hutter
BOHB: Robust and Efficient Hyperparameter Optimization at Scale. CoRR (2018)
Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, Frank Hutter
Learning Curve Prediction with Bayesian Neural Networks. ICLR (Poster) (2017)
Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. AISTATS (2017)
Klaus Greff, Aaron Klein, Martin Chovanec, Frank Hutter, Jürgen Schmidhuber
The Sacred Infrastructure for Computational Research. SciPy (2017)
Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. CoRR (2016)
Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter
Asynchronous Stochastic Gradient MCMC with Elastic Coupling. CoRR (2016)
Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter
Bayesian Optimization with Robust Bayesian Neural Networks. NIPS (2016)
Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Frank Hutter
Towards Automatically-Tuned Neural Networks. AutoML@ICML (2016)
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter
Efficient and Robust Automated Machine Learning. NIPS (2015)