Menu

Alumni

Aaron Klein

Postal address
Institut für Informatik
Albert-Ludwigs-Universität Freiburg
Sekretariat Hutter/Maschinelles Lernen
Georges-Köhler-Allee 074
79110 Freiburg, Germany
Fax
+49 761 203-74217
Office
Building 74, Room 00-016
kleinaa@cs.uni-freiburg.de
Publications

About

I am a PhD student at the Computer Vision Group and the Research Group for Automated Algorithm Design under the supervision of Frank Hutter and Thomas Brox. Before that I did my master degree at the University of Freiburg.

Research Interests

My research focuses on new ways to construct robust automated (deep) learning systems, that can learn with almost no human intervention. More precisely my research areas include:

  • Bayesian optimization
  • Automated hyperparameter optimization and architecture search
  • Bayesian deep neural networks
  • General deep learning

Code

  • RoBO: A python framework for Bayesian optimization. It also contains code for Fabolas and Bohamiann
  • HPOlib2: A set of benchmarks for automated hyperparameter optimization

Datasets

  • LC-dataset: Learning curves from different machine learning algorithms that we used as benchmarks in our LC-Net paper

Publications

2021

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

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

In: Vanschoren, J.; Yeung, S. (Ed.): Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, 2021.

Siems, Julien; Klein, Aaron; Archambeau, Cedric; Mahsereci, Maren

Dynamic Pruning of a Neural Network via Gradient Signal-to-Noise Ratio Conference

AutoML Workshop at ICML 2021, 2021.

2019

Gargiani, M; Klein, A; Falkner, S; Hutter, F

Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings Inproceedings

In: 6th ICML Workshop on Automated Machine Learning, 2019.

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

Auto-sklearn: Efficient and Robust Automated Machine Learning Incollection

In: Hutter, Frank; Kotthoff, Lars; Vanschoren, Joaquin (Ed.): AutoML: Methods, Systems, Challenges, pp. 113–134, Springer, 2019.

Mendoza, Hector; Klein, Aaron; Feurer, Matthias; Springenberg, Jost Tobias; Urban, Matthias; Burkart, Michael; Dippel, Max; Lindauer, Marius; Hutter, Frank

Towards Automatically-Tuned Deep Neural Networks Incollection

In: Hutter, Frank; Kotthoff, Lars; Vanschoren, Joaquin (Ed.): AutoML: Methods, Sytems, Challenges, pp. 135–149, Springer, 2019.

Klein, Aaron; Dai, Zhenwen; Hutter, Frank; Lawrence, Neil; Gonzalez, Javier

Meta-Surrogate Benchmarking for Hyperparameter Optimization Incollection

In: Wallach, H; Larochelle, H; Beygelzimer, A; d' Alché-Buc, F; Fox, E; Garnett, R (Ed.): Advances in Neural Information Processing Systems 32, pp. 6270–6280, Curran Associates, Inc., 2019.

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

Nas-bench-101: Towards reproducible neural architecture search Inproceedings

In: Thirty-sixth International Conference on Machine Learning, 2019.

2018

Zela, Arber; Klein, Aaron; Falkner, Stefan; Hutter, Frank

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

In: ICML 2018 AutoML Workshop, 2018.

Falkner, Stefan; Klein, Aaron; Hutter, Frank

BOHB: Robust and Efficient Hyperparameter Optimization at Scale Inproceedings

In: Proceedings of the 35th International Conference on Machine Learning (ICML 2018), pp. 1436–1445, 2018.

Ilg, Eddy; Cicek, Oezguen; Galesso, Silvio; Klein, Aaron; Makansi, Osama; Hutter, Frank; Brox, Thomas

Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks Journal Article

In: Proceedings of ECCV 2018, 2018.

2017

Klein, A; Falkner, S; Mansur, N; Hutter, F

RoBO: A Flexible and Robust Bayesian Optimization Framework in Python Inproceedings

In: NIPS 2017 Bayesian Optimization Workshop, 2017.

Falkner, S; Klein, A; Hutter, F

Combining Hyperband and Bayesian Optimization Inproceedings

In: NIPS 2017 Bayesian Optimization Workshop, 2017.

Greff, K; Klein, A; Chovanec, M; Hutter, F; Schmidhuber, J

The Sacred Infrastructure for Computational Research Inproceedings

In: Proceedings of the 15th Python in Science Conference (SciPy 2017), 2017.

Klein, A; Falkner, S; Springenberg, J T; Hutter, F

Learning Curve Prediction with Bayesian Neural Networks Inproceedings

In: International Conference on Learning Representations (ICLR) 2017 Conference Track, 2017.

Klein, A; Falkner, S; Bartels, S; Hennig, P; Hutter, F

Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets Inproceedings

In: Proceedings of the AISTATS conference, 2017.

Klein, A; Falkner, S; Bartels, S; Hennig, P; Hutter, F

Fast Bayesian hyperparameter optimization on large datasets Inproceedings

In: Electronic Journal of Statistics, 2017.

2016

Springenberg, J T; Klein, A; Falkner, S; Hutter, F

Bayesian optimization with robust Bayesian neural networks Inproceedings

In: Advances in Neural Information Processing Systems 29, 2016.

Mendoza, H; Klein, A; Feurer, M; Springenberg, J; Hutter, F

Towards Automatically-Tuned Neural Networks Inproceedings

In: ICML 2016 AutoML Workshop, 2016.

2015

Klein, A; Bartels, S; Falkner, S; Hennig, P; Hutter, F

Towards efficient Bayesian Optimization for Big Data Inproceedings

In: NIPS 2015 Bayesian Optimization Workshop, 2015.

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

Efficient and Robust Automated Machine Learning Inproceedings

In: Advances in Neural Information Processing Systems 28 (NeurIPS'15), pp. 2962–2970, 2015.

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

Methods for Improving Bayesian Optimization for AutoML Inproceedings

In: ICML 2015 AutoML Workshop, 2015.