Frank Hutter

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University of Freiburg ∙ The University of British Columbia

Professor Of Computer Science at University of Freiburg

Multi-headed Neural Ensemble Search

Ensembles of CNN models trained with different seeds (also known as Deep...
07/09/2021 ∙ by Ashwin Raaghav Narayanan, et al. ∙ 0 ∙ share

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Bag of Tricks for Neural Architecture Search

While neural architecture search methods have been successful in previou...
07/08/2021 ∙ by Thomas Elsken, et al. ∙ 0 ∙ share

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Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data

Tabular datasets are the last "unconquered castle" for deep learning, wi...
06/21/2021 ∙ by Arlind Kadra, et al. ∙ 0 ∙ share

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TempoRL: Learning When to Act

Reinforcement learning is a powerful approach to learn behaviour through...
06/09/2021 ∙ by André Biedenkapp, et al. ∙ 0 ∙ share

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Self-Paced Context Evaluation for Contextual Reinforcement Learning

Reinforcement learning (RL) has made a lot of advances for solving a sin...
06/09/2021 ∙ by Theresa Eimer, et al. ∙ 0 ∙ share

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DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization

Modern machine learning algorithms crucially rely on several design deci...
05/20/2021 ∙ by Noor Awad, et al. ∙ 15 ∙ share

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DACBench: A Benchmark Library for Dynamic Algorithm Configuration

Dynamic Algorithm Configuration (DAC) aims to dynamically control a targ...
05/18/2021 ∙ by Theresa Eimer, et al. ∙ 0 ∙ share

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Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization

Neural architecture search (NAS) and hyperparameter optimization (HPO) m...
05/03/2021 ∙ by Julia Guerrero-Viu, et al. ∙ 0 ∙ share

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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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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

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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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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

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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

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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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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

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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

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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

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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

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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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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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!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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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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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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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 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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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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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-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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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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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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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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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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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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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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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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Frank Hutter

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