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Alumni, Alumni 2022

Katharina Eggensperger

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 00-012
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Short Bio

Katharina Eggensperger is a PhD student in the Machine Learning Lab at the University of Freiburg, Germany. Her interests focus on empirical performance modelling, automated machine learning and hyperparameter optimization. With her research, she aims to make machine learning easy-to-use. She has been an invited speaker at the BayesOpt workshop at NeurIPS 2016 and co-organized the AutoML workshop (now AutoML-Conf) in 2019-2022.

Code / Projects

  • HPOBench [paper] – collection of multi-fidelity hyperparameter optimization benchmarks (superseeds HPOlib)
  • auto-sklearn [paper, paper] –  automated machine learning in Python: “AutoML in 4 lines of Code”
  • SMAC3 [paper] – Bayesian optimization package (Python reimplementation of the SMAC package)
  • And others, see Github

Teaching

  • 2019: Teaching Assistant: Seminar on Bayesian Optimization
  • 2019: Teaching Assistant: Lecture on Automated Machine Learning
  • 2015: Teaching Assistant: Lab Course on Bayesian Optimization

Publications

2024

Kohli, Ravin; Feurer, Matthias; Eggensperger, Katharina; Bischl, Bernd; Hutter, Frank

Towards Quantifying the Effect of Datasets for Benchmarking: A Look at Tabular Machine Learning Inproceedings

In: Data-centric Machine Learning Research (DMLR) Workshop (ICLR 2024), 2024.

Bergman, Edward; Feurer, Matthias; Bahram, Aron; Balef, Amir Rezaei; Purucker, Lennart; Segel, Sarah; Lindauer, Marius; Hutter, Frank; Eggensperger, Katharina

AMLTK: A Modular AutoML Toolkit in Python Journal Article

In: Journal of Open Source Software, vol. 9, no. 100, pp. 6367, 2024.

2023

Hollmann, Noah; Müller, Samuel; Eggensperger, Katharina; Hutter, Frank

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second Inproceedings

In: The Eleventh International Conference on Learning Representations (ICLR), 2023, ( top-25% of accepted papers ).

Feurer, Matthias; Eggensperger, Katharina; Bergman, Edward; Pfisterer, Florian; Bischl, Bernd; Hutter, Frank

Mind the Gap: Measuring Generalization Performance Across Multiple Objectives Inproceedings

In: Crémilleux, Bruno; Hess, Sibylle; Nijssen, Siegfried (Ed.): Advances in Intelligent Data Analysis XXI. IDA 2023., pp. 130-142, Springer, Cham, 2023.

Weerts, Hilde; Pfisterer, Florian; Feurer, Matthias; Eggensperger, Katharina; Bergman, Edward; Awad, Noor; Vanschoren, Joaquin; Pechenizkiy, Mykola; Bischl, Bernd; Hutter, Frank

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML Journal Article

In: arXiv:2303.08485 [cs.AI], 2023.

2022

Feurer, Matthias; Eggensperger, Katharina; Falkner, Stefan; Lindauer, Marius; Hutter, Frank

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning Journal Article

In: Journal of Machine Learning Research, vol. 23, no. 261, pp. 1-61, 2022.

Eggensperger, Katharina

Advanced Hyperparameter Optimization: Performance Modelling and Efficient Benchmarking PhD Thesis

University of Freiburg, Department of Computer Science, 2022.

Lindauer, Marius; Eggensperger, Katharina; Feurer, Matthias; Biedenkapp, André; Deng, Difan; Benjamins, Carolin; Ruhkopf, Tim; Sass, René; Hutter, Frank

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization Journal Article

In: Journal of Machine Learning Research (JMLR) -- MLOSS, vol. 23, no. 54, pp. 1-9, 2022.

Hollmann, Noah; Müller, Samuel; Eggensperger, Katharina; Hutter, Frank

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second Inproceedings

In: NeurIPS 2022 First Table Representation Workshop, 2022.

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.

2020

Awad, Noor; Shala, Gresa; Deng, Difan; Mallik, Neeratyoy; Feurer, Matthias; Eggensperger, Katharina; Biedenkapp, André; Vermetten, Diederick; Wang, Hao; Doerr, Carola; Lindauer, Marius; Hutter, Frank

Squirrel: A Switching Hyperparameter Optimizer Description of the entry by AutoML.org & IOHprofiler to the NeurIPS 2020 BBO challenge Journal Article

In: arXiv:2012.08180 [cs.LG], 2020, (Optimizer description for the NeurIPS 2020 BBO competition. Squirrel won the competition´s warm-starting friendly leaderboard.).

Eggensperger, Katharina; Haase, Kai; Müller, Philipp; Lindauer, Marius; Hutter, Frank

Neural Model-based Optimization with Right-Censored Observations Journal Article

In: arXiv:2009:13828 [cs.AI], 2020.

2019

Lindauer, Marius; Eggensperger, Katharina; Feurer, Matthias; Biedenkapp, André; Marben, Joshua; Müller, Philipp; Hutter, Frank

BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters Journal Article

In: arXiv:1908.06756 [cs.LG], 2019.

Lindauer, Marius; Feurer, Matthias; Eggensperger, Katharina; Biedenkapp, André; Hutter, Frank

Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters Inproceedings

In: IJCAI 2019 DSO Workshop, 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.

Eggensperger, Katharina; Lindauer, Marius; Hutter, Frank

Pitfalls and Best Practices in Algorithm Configuration Journal Article

In: Journal of Artificial Intelligence Research (JAIR), vol. 64, pp. 861–893, 2019.

2018

Eggensperger, Katharina; Lindauer, Marius; Hutter, Frank

Neural Networks for Predicting Algorithm Runtime Distributions Inproceedings

In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’18), pp. 1442-1448, 2018.

Feurer, Matthias; Eggensperger, Katharina; Falkner, Stefan; Lindauer, Marius; Hutter, Frank

Practical Automated Machine Learning for the AutoML Challenge 2018 Inproceedings

In: ICML 2018 AutoML Workshop, 2018.

Eggensperger, Katharina; Lindauer, Marius; Hoos, Holger H; Hutter, Frank; Leyton-Brown, Kevin

Efficient Benchmarking of Algorithm Configurators via Model-Based Surrogates Journal Article

In: Machine Learning, vol. 107, pp. 15-41, 2018.

2017

Martinez-Cantin, Ruben; Tee, Kevin; McCourt, Mike; Eggensperger, Katharina

Filtering Outliers in Bayesian Optimization Inproceedings

In: NeuriPS workshop on Bayesian Optimization (BayesOpt'17), 2017.

Biedenkapp, André; Lindauer, Marius; Eggensperger, Katharina; Fawcett, Chris; Hoos, Holger H; Hutter, Frank

Efficient Parameter Importance Analysis via Ablation with Surrogates Inproceedings

In: Proceedings of the Thirty-First Conference on Artificial Intelligence (AAAI'17), pp. 773–779, 2017.

Schirrmeister, Robin; Springenberg, Jost Tobias; Fiederer, Lukas; Glasstetter, Martin; Eggensperger, Katharina; Tangermann, Michael; Hutter, Frank; Burgard, Wolfram; Ball, Tonio

Deep learning with convolutional neural networks for EEG decoding and visualization Journal Article

In: Human Brain Mapping, vol. 38, pp. 5391–5420, 2017.

2016

Meinel, Andreas; Eggensperger, Katharina; Tangermann, Michael; Hutter, Frank

Hyperparameter Optimization for Machine Learning Problems in BCI (Abstract) Inproceedings

In: Proceedings of the International Brain Computer Interface Meeting 2016, 2016.

Schubert, Tobias; Eggensperger, Katharina; Gkogkidis, Alexis; Hutter, Frank; Ball, Tonio; Burgard, Wolfram

Automatic Bone Parameter Estimation for Skeleton Tracking in Optical Motion Capture Inproceedings

In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'16), 2016, (Video showing the results of the optimization procedure).

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.

Eggensperger, K; Hutter, F; Hoos, H H; Leyton-Brown, K

Efficient Benchmarking of Hyperparameter Optimizers via Surrogates Inproceedings

In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.

2014

Eggensperger, Katharina; Hutter, Frank; Hoos, Holger H; Leyton-Brown, Kevin

Surrogate Benchmarks for Hyperparameter Optimization Inproceedings

In: ECAI workshop on Metalearning and Algorithm Selection (MetaSel), pp. 24-31, 2014, (Superseeded by the AAAI15 paper _Efficient Benchmarking of Hyperparameter Optimizers via Surrogates_).

2013

Eggensperger, Katharina; Feurer, Matthias; Hutter, Frank; Bergstra, James; Snoek, Jasper; Hoos, Holger H; Leyton-Brown, Kevin

Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters Inproceedings

In: NeurIPS workshop on Bayesian Optimization in Theory and Practice, 2013, (Software and benchmarks are available from our HPOlib website.).