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ORCIDAaron Klein
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2020 – today
- 2024
[i18]Aaron Klein, Jacek Golebiowski, Xingchen Ma, Valerio Perrone, Cédric Archambeau:
Structural Pruning of Pre-trained Language Models via Neural Architecture Search. CoRR abs/2405.02267 (2024)- 2023
- [j1]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): 877-905 (2023) - [c17]David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau:
Optimizing Hyperparameters with Conformal Quantile Regression. ICML 2023: 29876-29893 - [i17]David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau:
Optimizing Hyperparameters with Conformal Quantile Regression. CoRR abs/2305.03623 (2023) - [i16]Sigrid Passano Hellan, Huibin Shen, François-Xavier Aubet, David Salinas, Aaron Klein:
Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation. CoRR abs/2306.16916 (2023) - 2022
- [c16]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: 7/1-21 - [c15]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: 16/1-23 - 2021
- [c14]Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau:
Hyperparameter Transfer Learning with Adaptive Complexity. AISTATS 2021: 1378-1386 - [c13]Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos:
BORE: Bayesian Optimization by Density-Ratio Estimation. ICML 2021: 10289-10300 - [c12]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 - [i15]Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos:
BORE: Bayesian Optimization by Density-Ratio Estimation. CoRR abs/2102.09009 (2021) - [i14]Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau:
Hyperparameter Transfer Learning with Adaptive Complexity. CoRR abs/2102.12810 (2021) - [i13]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 abs/2104.08166 (2021) - [i12]Jan Sosulski, David Hübner, Aaron Klein, Michael Tangermann:
Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints. CoRR abs/2109.06011 (2021) - [i11]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 abs/2109.06716 (2021) - 2020
- [b1]Aaron Klein:
Efficient bayesian hyperparameter optimization. University of Freiburg, Germany, 2020 - [i10]Louis C. Tiao, Aaron Klein, Cédric Archambeau, Matthias W. Seeger:
Model-based Asynchronous Hyperparameter Optimization. CoRR abs/2003.10865 (2020)
2010 – 2019
- 2019
- [c11]Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, Frank Hutter:
NAS-Bench-101: Towards Reproducible Neural Architecture Search. ICML 2019: 7105-7114 - [c10]Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier González:
Meta-Surrogate Benchmarking for Hyperparameter Optimization. NeurIPS 2019: 6267-6277 - [p2]Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter:
Auto-sklearn: Efficient and Robust Automated Machine Learning. Automated Machine Learning 2019: 113-134 - [p1]Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart, Maximilian Dippel, Marius Lindauer, Frank Hutter:
Towards Automatically-Tuned Deep Neural Networks. Automated Machine Learning 2019: 135-149 - [i9]Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, Frank Hutter:
NAS-Bench-101: Towards Reproducible Neural Architecture Search. CoRR abs/1902.09635 (2019) - [i8]Aaron Klein, Frank Hutter:
Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization. CoRR abs/1905.04970 (2019) - [i7]Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier González:
Meta-Surrogate Benchmarking for Hyperparameter Optimization. CoRR abs/1905.12982 (2019) - [i6]Matilde Gargiani, Aaron Klein, Stefan Falkner, Frank Hutter:
Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings. CoRR abs/1910.04522 (2019) - 2018
- [c9]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: 677-693 - [c8]Stefan Falkner, Aaron Klein, Frank Hutter:
Practical Hyperparameter Optimization for Deep Learning. ICLR (Workshop) 2018 - [c7]Stefan Falkner, Aaron Klein, Frank Hutter:
BOHB: Robust and Efficient Hyperparameter Optimization at Scale. ICML 2018: 1436-1445 - [i5]Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox:
Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks. CoRR abs/1802.07095 (2018) - [i4]Stefan Falkner, Aaron Klein, Frank Hutter:
BOHB: Robust and Efficient Hyperparameter Optimization at Scale. CoRR abs/1807.01774 (2018) - [i3]Arber Zela, Aaron Klein, Stefan Falkner, Frank Hutter:
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search. CoRR abs/1807.06906 (2018) - 2017
- [c6]Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter:
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. AISTATS 2017: 528-536 - [c5]Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, Frank Hutter:
Learning Curve Prediction with Bayesian Neural Networks. ICLR (Poster) 2017 - [c4]Klaus Greff, Aaron Klein, Martin Chovanec, Frank Hutter, Jürgen Schmidhuber:
The Sacred Infrastructure for Computational Research. SciPy 2017: 49-56 - 2016
- [c3]Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Frank Hutter:
Towards Automatically-Tuned Neural Networks. AutoML@ICML 2016: 58-65 - [c2]Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter:
Bayesian Optimization with Robust Bayesian Neural Networks. NIPS 2016: 4134-4142 - [i2]Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter:
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. CoRR abs/1605.07079 (2016) - [i1]Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter:
Asynchronous Stochastic Gradient MCMC with Elastic Coupling. CoRR abs/1612.00767 (2016) - 2015
- [c1]Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter:
Efficient and Robust Automated Machine Learning. NIPS 2015: 2962-2970
Coauthor Index
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last updated on 2024-10-07 21:23 CEST by the dblp team
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