Frank Hutter

is this you? claim profile

∙ 0 followers
University of Freiburg ∙ The University of British Columbia

Professor Of Computer Science at University of Freiburg

TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation

Automatic augmentation methods have recently become a crucial pillar for...
03/18/2021 ∙ by Samuel G. Müller, et al. ∙ 0 ∙ share

read it
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning

Model-based Reinforcement Learning (MBRL) is a promising framework for l...
02/26/2021 ∙ by Baohe Zhang, et al. ∙ 0 ∙ share

read it
In-Loop Meta-Learning with Gradient-Alignment Reward

At the heart of the standard deep learning training loop is a greedy gra...
02/05/2021 ∙ by Samuel Müller, et al. ∙ 0 ∙ share

read it
Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies

This work explores learning agent-agnostic synthetic environments (SEs) ...
01/24/2021 ∙ by Fabio Ferreira, et al. ∙ 0 ∙ share

read it
Squirrel: A Switching Hyperparameter Optimizer

In this short note, we describe our submission to the NeurIPS 2020 BBO c...
12/15/2020 ∙ by Noor Awad, et al. ∙ 26 ∙ share

read it
Differential Evolution for Neural Architecture Search

Neural architecture search (NAS) methods rely on a search strategy for d...
12/11/2020 ∙ by Noor Awad, et al. ∙ 16 ∙ share

read it
Convergence Analysis of Homotopy-SGD for non-convex optimization

First-order stochastic methods for solving large-scale non-convex optimi...
11/20/2020 ∙ by Matilde Gargiani, et al. ∙ 0 ∙ share

read it
Hyperparameter Transfer Across Developer Adjustments

After developer adjustments to a machine learning (ML) algorithm, how ca...
10/25/2020 ∙ by Danny Stoll, et al. ∙ 0 ∙ share

read it
On the Importance of Domain Model Configuration for Automated Planning Engines

The development of domain-independent planners within the AI Planning co...
10/15/2020 ∙ by Mauro Vallati, et al. ∙ 0 ∙ share

read it
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search

In this paper, we propose an approach to neural architecture search (NAS...
10/09/2020 ∙ by Jovita Lukasik, et al. ∙ 0 ∙ share

read it
Neural Model-based Optimization with Right-Censored Observations

In many fields of study, we only observe lower bounds on the true respon...
09/29/2020 ∙ by Katharina Eggensperger, et al. ∙ 0 ∙ share

read it
Sample-Efficient Automated Deep Reinforcement Learning

Despite significant progress in challenging problems across various doma...
09/03/2020 ∙ by Jörg K. H. Franke, et al. ∙ 0 ∙ share

read it
NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search

Neural Architecture Search (NAS) is a logical next step in the automatic...
08/22/2020 ∙ by Julien Siems, et al. ∙ 0 ∙ share

read it
Auto-Sklearn 2.0: The Next Generation

Automated Machine Learning, which supports practitioners and researchers...
07/08/2020 ∙ by Matthias Feurer, et al. ∙ 0 ∙ share

read it
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

read it
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. ∙ 105 ∙ share

read it
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

read it
Learning Heuristic Selection with Dynamic Algorithm Configuration

A key challenge in satisfying planning is to use multiple heuristics wit...
06/15/2020 ∙ by David Speck, et al. ∙ 0 ∙ share

read it
On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs

Following early work on Hessian-free methods for deep learning, we study...
06/03/2020 ∙ by Matilde Gargiani, et al. ∙ 0 ∙ share

read it
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

read it
NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search

One-shot neural architecture search (NAS) has played a crucial role in m...
01/28/2020 ∙ by Arber Zela, et al. ∙ 0 ∙ share

read it
Meta-Learning of Neural Architectures for Few-Shot Learning

The recent progress in neural architectures search (NAS) has allowed sca...
11/25/2019 ∙ by Thomas Elsken, et al. ∙ 0 ∙ share

read it
OpenML-Python: an extensible Python API for OpenML

OpenML is an online platform for open science collaboration in machine l...
11/06/2019 ∙ by Matthias Feurer, et al. ∙ 0 ∙ share

read it
Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control

Current Deep Reinforcement Learning algorithms still heavily rely on han...
10/28/2019 ∙ by Jörg K. H. Franke, et al. ∙ 0 ∙ share

read it
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

read it
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

read it
!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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
Learning to Design RNA

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

read it
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

read it
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

read it
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

read it
Generative Reversible Networks

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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it

Frank Hutter

Featured Co-authors