Frank Hutter

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University of Freiburg ∙ The University of British Columbia

Professor Of Computer Science at University of Freiburg

Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL

While early AutoML frameworks focused on optimizing traditional ML pipel...
06/24/2020 ∙ by Lucas Zimmer, et al. ∙ 104 ∙ share

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Prior-guided Bayesian Optimization

While Bayesian Optimization (BO) is a very popular method for optimizing...
06/25/2020 ∙ by Artur Souza, et al. ∙ 53 ∙ share

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Neural Ensemble Search for Performant and Calibrated Predictions

Ensembles of neural networks achieve superior performance compared to st...
06/15/2020 ∙ by Sheheryar Zaidi, et al. ∙ 15 ∙ share

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Machine-Learning-Based Diagnostics of EEG Pathology

Machine learning (ML) methods have the potential to automate clinical EE...
02/11/2020 ∙ by Lukas Alexander Wilhelm Gemein, et al. ∙ 13 ∙ share

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Generative Reversible Networks

Generative models with an encoding component such as autoencoders curren...
06/05/2018 ∙ by Robin Tibor Schirrmeister, et al. ∙ 2 ∙ share

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Simple And Efficient Architecture Search for Convolutional Neural Networks

Neural networks have recently had a lot of success for many tasks. Howev...
11/13/2017 ∙ by Thomas Elsken, et al. ∙ 0 ∙ share

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Predicting Runtime Distributions using Deep Neural Networks

Many state-of-the-art algorithms for solving hard combinatorial problems...
09/22/2017 ∙ by Katharina Eggensperger, et al. ∙ 0 ∙ share

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Warmstarting of Model-based Algorithm Configuration

The performance of many hard combinatorial problem solvers depends stron...
09/14/2017 ∙ by Marius Lindauer, et al. ∙ 0 ∙ share

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Deep learning with convolutional neural networks for EEG decoding and visualization

A revised version of this article is now available at Human Brain Mappin...
03/15/2017 ∙ by Robin Tibor Schirrmeister, et al. ∙ 0 ∙ share

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SGDR: Stochastic Gradient Descent with Warm Restarts

Restart techniques are common in gradient-free optimization to deal with...
08/13/2016 ∙ by Ilya Loshchilov, et al. ∙ 0 ∙ share

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

We apply convolutional neural networks (ConvNets) to the task of disting...
08/26/2017 ∙ by Robin Tibor Schirrmeister, et al. ∙ 0 ∙ share

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CMA-ES for Hyperparameter Optimization of Deep Neural Networks

Hyperparameters of deep neural networks are often optimized by grid sear...
04/25/2016 ∙ by Ilya Loshchilov, et al. ∙ 0 ∙ share

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OpenML Benchmarking Suites and the OpenML100

We advocate the use of curated, comprehensive benchmark suites of machin...
08/11/2017 ∙ by Bernd Bischl, et al. ∙ 0 ∙ share

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Pitfalls and Best Practices in Algorithm Configuration

Good parameter settings are crucial to achieve high performance in many ...
05/17/2017 ∙ by Katharina Eggensperger, et al. ∙ 0 ∙ share

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Online Batch Selection for Faster Training of Neural Networks

Deep neural networks are commonly trained using stochastic non-convex op...
11/19/2015 ∙ by Ilya Loshchilov, et al. ∙ 0 ∙ share

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A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets

The original ImageNet dataset is a popular large-scale benchmark for tra...
07/27/2017 ∙ by Patryk Chrabaszcz, et al. ∙ 0 ∙ share

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Asynchronous Stochastic Gradient MCMC with Elastic Coupling

We consider parallel asynchronous Markov Chain Monte Carlo (MCMC) sampli...
12/02/2016 ∙ by Jost Tobias Springenberg, et al. ∙ 0 ∙ share

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Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates

The optimization of algorithm (hyper-)parameters is crucial for achievin...
03/30/2017 ∙ by Katharina Eggensperger, et al. ∙ 0 ∙ share

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A case study of algorithm selection for the traveling thief problem

Many real-world problems are composed of several interacting components....
09/02/2016 ∙ by Markus Wagner, et al. ∙ 0 ∙ share

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The reparameterization trick for acquisition functions

Bayesian optimization is a sample-efficient approach to solving global o...
12/01/2017 ∙ by James T. Wilson, et al. ∙ 0 ∙ share

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ASlib: A Benchmark Library for Algorithm Selection

The task of algorithm selection involves choosing an algorithm from a se...
06/08/2015 ∙ by Bernd Bischl, et al. ∙ 0 ∙ share

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The Configurable SAT Solver Challenge (CSSC)

It is well known that different solution strategies work well for differ...
05/05/2015 ∙ by Frank Hutter, et al. ∙ 0 ∙ share

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Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

Bayesian optimization has become a successful tool for hyperparameter op...
05/23/2016 ∙ by Aaron Klein, et al. ∙ 0 ∙ share

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ParamILS: An Automatic Algorithm Configuration Framework

The identification of performance-optimizing parameter settings is an im...
01/15/2014 ∙ by Frank Hutter, et al. ∙ 0 ∙ share

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SATzilla: Portfolio-based Algorithm Selection for SAT

It has been widely observed that there is no single "dominant" SAT solve...
10/31/2011 ∙ by Lin Xu, et al. ∙ 0 ∙ share

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Bayesian Optimization With Censored Response Data

Bayesian optimization (BO) aims to minimize a given blackbox function us...
10/07/2013 ∙ by Frank Hutter, et al. ∙ 0 ∙ share

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Bayesian Optimization in a Billion Dimensions via Random Embeddings

Bayesian optimization techniques have been successfully applied to robot...
01/09/2013 ∙ by Ziyu Wang, et al. ∙ 0 ∙ share

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Algorithm Runtime Prediction: Methods & Evaluation

Perhaps surprisingly, it is possible to predict how long an algorithm wi...
11/05/2012 ∙ by Frank Hutter, et al. ∙ 0 ∙ share

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Fixing Weight Decay Regularization in Adam

We note that common implementations of adaptive gradient algorithms, suc...
11/14/2017 ∙ by Ilya Loshchilov, et al. ∙ 0 ∙ share

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Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks

Recent work has shown that optical flow estimation can be formulated as ...
02/20/2018 ∙ by Eddy Ilg, et al. ∙ 0 ∙ share

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Multi-objective Architecture Search for CNNs

Architecture search aims at automatically finding neural architectures t...
04/24/2018 ∙ by Thomas Elsken, et al. ∙ 0 ∙ share

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Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari

Evolution Strategies (ES) have recently been demonstrated to be a viable...
02/24/2018 ∙ by Patryk Chrabaszcz, et al. ∙ 0 ∙ share

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Maximizing acquisition functions for Bayesian optimization

Bayesian optimization is a sample-efficient approach to global optimizat...
05/25/2018 ∙ by James T. Wilson, et al. ∙ 0 ∙ share

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BOHB: Robust and Efficient Hyperparameter Optimization at Scale

Modern deep learning methods are very sensitive to many hyperparameters,...
07/04/2018 ∙ by Stefan Falkner, et al. ∙ 0 ∙ share

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Training Generative Reversible Networks

Generative models with an encoding component such as autoencoders curren...
06/05/2018 ∙ by Robin Tibor Schirrmeister, et al. ∙ 0 ∙ share

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Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search

While existing work on neural architecture search (NAS) tunes hyperparam...
07/18/2018 ∙ by Arber Zela, et al. ∙ 0 ∙ share

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Neural Architecture Search: A Survey

Deep Learning has enabled remarkable progress over the last years on a v...
08/16/2018 ∙ by Thomas Elsken, et al. ∙ 0 ∙ share

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Learning to Design RNA

Designing RNA molecules has garnered recent interest in medicine, synthe...
12/31/2018 ∙ by Frederic Runge, et al. ∙ 0 ∙ share

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NAS-Bench-101: Towards Reproducible Neural Architecture Search

Recent advances in neural architecture search (NAS) demand tremendous co...
02/25/2019 ∙ by Chris Ying, et al. ∙ 0 ∙ share

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Meta-Learning Acquisition Functions for Bayesian Optimization

Many practical applications of machine learning require data-efficient b...
04/04/2019 ∙ by Michael Volpp, et al. ∙ 0 ∙ share

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Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization

Due to the high computational demands executing a rigorous comparison be...
05/13/2019 ∙ by Aaron Klein, et al. ∙ 0 ∙ share

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Meta-Surrogate Benchmarking for Hyperparameter Optimization

Despite the recent progress in hyperparameter optimization (HPO), availa...
05/30/2019 ∙ by Aaron Klein, et al. ∙ 0 ∙ share

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AutoDispNet: Improving Disparity Estimation with AutoML

Much research work in computer vision is being spent on optimizing exist...
05/17/2019 ∙ by Tonmoy Saikia, et al. ∙ 0 ∙ share

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Towards White-box Benchmarks for Algorithm Control

The performance of many algorithms in the fields of hard combinatorial p...
06/18/2019 ∙ by André Biedenkapp, et al. ∙ 0 ∙ share

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

Hyperparameter optimization and neural architecture search can become pr...
08/16/2019 ∙ by Marius Lindauer, et al. ∙ 0 ∙ share

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Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters

Bayesian Optimization (BO) is a common approach for hyperparameter optim...
08/19/2019 ∙ by Marius Lindauer, et al. ∙ 0 ∙ share

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Best Practices for Scientific Research on Neural Architecture Search

We describe a set of best practices for the young field of neural archit...
09/05/2019 ∙ by Marius Lindauer, et al. ∙ 0 ∙ share

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!MDP Playground: Meta-Features in Reinforcement Learning

Reinforcement Learning (RL) algorithms usually assume their environment ...
09/17/2019 ∙ by Raghu Rajan, et al. ∙ 0 ∙ share

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Understanding and Robustifying Differentiable Architecture Search

Differentiable Architecture Search (DARTS) has attracted a lot of attent...
09/20/2019 ∙ by Arber Zela, et al. ∙ 0 ∙ share

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Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings

We propose probabilistic models that can extrapolate learning curves of ...
10/10/2019 ∙ by Matilde Gargiani, et al. ∙ 0 ∙ share

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Frank Hutter

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