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2018

  • Eggensperger, K. and Lindauer, M. and Hutter, F. (arXiv)(published)(poster)(slides)(bib)
    Neural Networks for Predicting Algorithm Runtime Distributions
    In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’18)
  • Feurer, M. and Eggensperger, K. and Falkner, S. and Lindauer, M. and Hutter, F. (pdf)(bib)
    Practical Automated Machine Learning for the AutoML Challenge 2018
    In: ICML 2018 AutoML Workshop
  • Eggensperger, K. and Lindauer, M. and Hoos, H. and Hutter, F. and Leyton-Brown, K (arXiv)(published)(bib)
    Efficient Benchmarking of Algorithm Configurators via Model-Based Surrogates
    In: Machine Learning 107 (2018): 15-41

2017

  • Ruben Martinez-Cantin and Kevin Tee and Mike Mccourt and Katharina Eggensperger (pdf)(supplementary)(poster)(bib)
    Filtering Outliers in Bayesian Optimization
    In: NIPS workshop on Bayesian Optimization (BayesOpt'17)
  • Biedenkapp, A. and Lindauer, M. and Eggensperger, K. and Fawcett, C. and Hoos, H. and Hutter, F. (pdf)(poster)(bib)
    Efficient Parameter Importance Analysis via Ablation with Surrogates
    In: Proceedings of the AAAI conference
  • Eggensperger, K. and Lindauer, M. and Hutter, F. (arXiv)(bib)
    Pitfalls and Best Practices in Algorithm Configuration
    In: arXiv [cs.AI] 1705.06058 (2017): 1-23
  • Schirrmeister, R. and Springenberg, T. and Fiederer, L. and Glasstetter, M. and Eggensperger, K. and Tangermann, M. and Hutter, F. and Burgard, W. and Ball, T. (arXiv)(published)(bib)
    Deep learning with convolutional neural networks for EEG decoding and visualization
    In: Human Brain Mapping 38 (2017): 5391--5420

2016

  • Meinel, A. and Eggensperger, K. and Tangermann, M. and Hutter, F. (pdf)(bib)
    Hyperparameter Optimization for Machine Learning Problems in BCI (Abstract)
    In: Proceedings of the International Brain Computer Interface Meeting 2016
  • Schubert, T. and Eggensperger, K. and Gkogkidis, A. and Hutter, F. and Ball, T. and Burgard, W. (pdf)(bib)
    Automatic Bone Parameter Estimation for Skeleton Tracking in Optical Motion Capture
    In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'16)
    Video showing the results of the optimization procedure

2015

  • Feurer, M. and Klein, A. and Eggensperger, K. and Springenberg, J. and Blum, M. and Hutter, F. (preprint)(published)(supplementary)(poster)(bib)
    Efficient and Robust Automated Machine Learning
    In: Advances in Neural Information Processing Systems 28
  • Feurer, M. and Klein, A. and Eggensperger, K. and Springenberg, J. and Blum, M. and Hutter, F. (pdf)(poster)(slides)(bib)
    Methods for Improving Bayesian Optimization for AutoML
    In: ICML 2015 AutoML Workshop
  • Katharina Eggensperger and Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown (pdf)(poster)(bib)
    Efficient Benchmarking of Hyperparameter Optimizers via Surrogates
    In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence

2014

  • Katharina Eggensperger and Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown (pdf)(slides)(bib)
    Surrogate Benchmarks for Hyperparameter Optimization
    In: ECAI workshop on Metalearning and Algorithm Selection (MetaSel)
    Superseeded by the AAAI15 paper Efficient Benchmarking of Hyperparameter Optimizers via Surrogates

2013

  • Katharina Eggensperger and Matthias Feurer and Frank Hutter and James Bergstra and Jasper Snoek and Holger Hoos and Kevin Leyton-Brown (pdf)(poster)(bib)
    Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters
    In: NIPS workshop on Bayesian Optimization in Theory and Practice
    Software and benchmarks are available from our HPOlib website.