Publications (37)
Deep learning with convolutional neural networks for EEG decoding and visualization
Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer +6
Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution
Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
ParamILS: An Automatic Algorithm Configuration Framework
Frank Hutter, Thomas Stuetzle, Kevin Leyton-Brown +1
Bayesian Optimization With Censored Response Data
Frank Hutter, Holger Hoos, Kevin Leyton-Brown
Warmstarting of Model-based Algorithm Configuration
Marius Lindauer, Frank Hutter
Simple And Efficient Architecture Search for Convolutional Neural Networks
Thomas Elsken, Jan-Hendrik Metzen, Frank Hutter
Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari
Patryk Chrabaszcz, Ilya Loshchilov, Frank Hutter
Online Batch Selection for Faster Training of Neural Networks
Ilya Loshchilov, Frank Hutter
A case study of algorithm selection for the traveling thief problem
Markus Wagner, Marius Lindauer, Mustafa Misir +2
CMA-ES for Hyperparameter Optimization of Deep Neural Networks
Ilya Loshchilov, Frank Hutter
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Aaron Klein, Stefan Falkner, Simon Bartels +2
Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow
Eddy Ilg, Ãzgün Ãiçek, Silvio Galesso +4
ASlib: A Benchmark Library for Algorithm Selection
Bernd Bischl, Pascal Kerschke, Lars Kotthoff +8
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
Patryk Chrabaszcz, Ilya Loshchilov, Frank Hutter
Bayesian Optimization in a Billion Dimensions via Random Embeddings
Ziyu Wang, Frank Hutter, Masrour Zoghi +2
Raiders of the Lost Architecture: Kernels for Bayesian Optimization in Conditional Parameter Spaces
Kevin Swersky, David Duvenaud, Jasper Snoek +2
The Configurable SAT Solver Challenge (CSSC)
Frank Hutter, Marius Lindauer, Adrian Balint +3
Pitfalls and Best Practices in Algorithm Configuration
Katharina Eggensperger, Marius Lindauer, Frank Hutter
Asynchronous Stochastic Gradient MCMC with Elastic Coupling
Jost Tobias Springenberg, Aaron Klein, Stefan Falkner +1
Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization
Aaron Klein, Frank Hutter
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Stefan Falkner, Aaron Klein, Frank Hutter
Neural Architecture Search: A Survey
Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
NAS-Bench-101: Towards Reproducible Neural Architecture Search
Chris Ying, Aaron Klein, Esteban Real +3
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search
Arber Zela, Aaron Klein, Stefan Falkner +1
A Kernel for Hierarchical Parameter Spaces
Frank Hutter, Michael A. Osborne
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates
Katharina Eggensperger, Marius Lindauer, Holger H. Hoos +2
Algorithm Runtime Prediction: Methods & Evaluation
Frank Hutter, Lin Xu, Holger H. Hoos +1
Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
Robin Tibor Schirrmeister, Lukas Gemein, Katharina Eggensperger +2
Decoupled Weight Decay Regularization
Ilya Loshchilov, Frank Hutter
Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms
Chris Thornton, Frank Hutter, Holger H. Hoos +1
SGDR: Stochastic Gradient Descent with Warm Restarts
Ilya Loshchilov, Frank Hutter
SATzilla: Portfolio-based Algorithm Selection for SAT
Lin Xu, Frank Hutter, Holger H. Hoos +1
Learning to Design RNA
Frederic Runge, Danny Stoll, Stefan Falkner +1
Training Generative Reversible Networks
Robin Tibor Schirrmeister, Patryk ChrabÄ szcz, Frank Hutter +1
The reparameterization trick for acquisition functions
James T. Wilson, Riccardo Moriconi, Frank Hutter +1
Neural Networks for Predicting Algorithm Runtime Distributions
Katharina Eggensperger, Marius Lindauer, Frank Hutter
Maximizing acquisition functions for Bayesian optimization
James T. Wilson, Frank Hutter, Marc Peter Deisenroth