Frank Hutter

Gemein LA, Schirrmeister RT, Chrabąszcz P, Wilson D, Boedecker J, Schulze-Bonhage A, Hutter F, and Ball T. Machine-learning-based diagnostics of EEG pathology. NeuroImage 2020;220:117021.

Elsken T, Metzen JH, and Hutter F. Neural architecture search: A survey. Journal of Machine Learning Research 2019;20:1–21.

Hutter F, Kotthoff L, and Vanschoren J, eds. Automated Machine Learning: Methods, Systems, Challenges. Cham: Springer, 2019.

Loshchilov I and Hutter F. Decoupled weight decay regularization. In: International Conference on Learning Representations (ICLR’19). 2019. URL: https://openreview.net/forum?id=Bkg6RiCqY7.

Falkner S, Klein A, and Hutter F. BOHB: Robust and efficient hyperparameter optimization at scale. In: Proceedings of the 35th International Conference on Machine Learning (ICML’18). Ed. by Dy J and Krause A. Vol. 80. PMLR, 2018:1437–1446.

Loshchilov I and Hutter F. SGDR: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR’17). 2017. URL: https://openreview.net/forum?id=Skq89Scxx.

Schirrmeister RT, Springenberg JT, Fiederer LDJ, Glasstetter M, Eggensperger K, Tangermann M, Hutter F, Burgard W, and Ball T. Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping 2017;38:5391–5420.

Feurer M, Klein A, Eggensperger K, Springenberg J, Blum M, and Hutter F. Efficient and robust automated machine learning. In: Advances in Neural Information Processing Systems 28 (NIPS’15). Ed. by Cortes C, Lawrence ND, Lee DD, Sugiyama M, and R G. 2015:2962–2970.

Hutter F, Hoos HH, and Leyton-Brown K. Sequential model-based optimization for general algorithm configuration. In: Proceedings of the 5th International Conference on Learning and Intelligent Optimization (LION’15). 2011:507–523.

Xu L, Hutter F, Hoos HH, and Leyton-Brown K. SATzilla: Portfolio-based algorithm selection for SAT. Journal of Artificial Intelligence Research 2008;32:565–606.