Katharina Eggensperger

发表

Practical Automated Machine Learning for the AutoML Challenge 2018

F. Hutter, Matthias Feurer, Katharina Eggensperger, 2018 .

Efficient and Robust Automated Machine Learning

Aaron Klein, Frank Hutter, Jost Tobias Springenberg, 2015, NIPS.

Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters

Katharina Eggensperger, Katharina Eggensperger, 2013 .

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

Tonio Ball, Frank Hutter, Robin Tibor Schirrmeister, 2017, 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).

Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG pdf

Wolfram Burgard, Tonio Ball, Frank Hutter, 2017, ArXiv.

L G ] 1 6 D ec 2 02 0 Squirrel : A Switching Hyperparameter Optimizer Description of the entry by AutoML . org & IOHprofiler to the NeurIPS 2020 BBO challenge

Noor H. Awad, F. Hutter, Matthias Feurer, 2020 .

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

F. Hutter, Matthias Feurer, Katharina Eggensperger, 2020, 2007.04074.

Filtering Outliers in Bayesian Optimization

Ruben Martinez-Cantin, Katharina Eggensperger, M. McCourt, 2017 .

Meta-Learning a Real-Time Tabular AutoML Method For Small Data

Samuel G. Müller, F. Hutter, Katharina Eggensperger, 2022, ArXiv.

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

F. Hutter, Katharina Eggensperger, Samuel Muller, 2022, 2207.01848.

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

F. Hutter, Matthias Feurer, Katharina Eggensperger, 2020, J. Mach. Learn. Res..

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

Samuel G. Müller, F. Hutter, Katharina Eggensperger, 2022, ICLR.

Mind the Gap: Measuring Generalization Performance Across Multiple Objectives

F. Hutter, Matthias Feurer, Katharina Eggensperger, 2022, 2212.04183.

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

F. Hutter, Matthias Feurer, Katharina Eggensperger, 2021, J. Mach. Learn. Res..

Auto-sklearn: Efficient and Robust Automated Machine Learning

Aaron Klein, Frank Hutter, Jost Tobias Springenberg, 2019, Automated Machine Learning.

Squirrel: A Switching Hyperparameter Optimizer

Noor H. Awad, F. Hutter, Matthias Feurer, 2020, ArXiv.

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

Marius Lindauer, Frank Hutter, Matthias Feurer, 2019, ArXiv.

Automatic bone parameter estimation for skeleton tracking in optical motion capture

Wolfram Burgard, Tonio Ball, Frank Hutter, 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

Efficient benchmarking of algorithm configurators via model-based surrogates

Kevin Leyton-Brown, Holger H. Hoos, Marius Lindauer, 2017, Machine Learning.

Efficient Benchmarking of Hyperparameter Optimizers via Surrogates

Kevin Leyton-Brown, Holger H. Hoos, Frank Hutter, 2015, AAAI.

Surrogate Benchmarks for Hyperparameter Optimization

Kevin Leyton-Brown, Holger H. Hoos, Frank Hutter, 2014, MetaSel@ECAI.

Neural Model-based Optimization with Right-Censored Observations pdf

Marius Lindauer, Frank Hutter, Katharina Eggensperger, 2020, ArXiv.

Deep learning with convolutional neural networks for EEG decoding and visualization

Wolfram Burgard, Tonio Ball, Frank Hutter, 2017, Human brain mapping.

Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates pdf

Marius Thomas Lindauer, Kevin Leyton-Brown, Holger H. Hoos, 2017, ArXiv.

Auto-Sklearn 2.0: The Next Generation pdf

Marius Lindauer, Frank Hutter, Matthias Feurer, 2020, ArXiv.

Pitfalls and Best Practices in Algorithm Configuration

Marius Lindauer, Frank Hutter, Katharina Eggensperger, 2017, J. Artif. Intell. Res..

Neural Networks for Predicting Algorithm Runtime Distributions

Marius Lindauer, Frank Hutter, Katharina Eggensperger, 2017, IJCAI.

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

Aaron Klein, Marius Lindauer, Frank Hutter, 2021, NeurIPS Datasets and Benchmarks.

Efficient benchmarking of algorithm configurators via model-based surrogates

F. Hutter, H. Hoos, Kevin Leyton-Brown, 2017, Machine Learning.

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

Noor H. Awad, Hilde J. P. Weerts, F. Hutter, 2023, J. Artif. Intell. Res..

Mind the Gap: Measuring Generalization Performance Across Multiple Objectives

F. Hutter, Matthias Feurer, Katharina Eggensperger, 2022, IDA.

Efficient Parameter Importance Analysis via Ablation with Surrogates

Marius Thomas Lindauer, Holger H. Hoos, Frank Hutter, 2017, AAAI.

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

Samuel G. Müller, F. Hutter, Katharina Eggensperger, 2022, ICLR.

Supplementary for: Deep learning with convolutional neural networks for EEG decoding and visualization

Jost Tobias Springenberg, F. Hutter, Katharina Eggensperger, 2017 .

Squirrel: A Switching Hyperparameter Optimizer pdf

Marius Lindauer, Carola Doerr, Frank Hutter, 2020, ArXiv.

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

F. Hutter, Matthias Feurer, Katharina Eggensperger, 2020, J. Mach. Learn. Res..

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

Noor H. Awad, F. Hutter, Matthias Feurer, 2021, NeurIPS Datasets and Benchmarks.

Auto-sklearn: Efficient and Robust Automated Machine Learning

F. Hutter, Matthias Feurer, Katharina Eggensperger, 2019, Automated Machine Learning.

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

Matthias Feurer, Katharina Eggensperger, B. Bischl, 2022 .