André Biedenkapp | DeepAI

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∙ 06/07/2022

DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning

Automated Machine Learning (AutoML) is used more than ever before to sup...

6 René Sass, et al. ∙

∙ 05/27/2022

Automated Dynamic Algorithm Configuration

The performance of an algorithm often critically depends on its paramete...

18 Steven Adriaensen, et al. ∙

∙ 02/09/2022

Contextualize Me – The Case for Context in Reinforcement Learning

While Reinforcement Learning (RL) has made great strides towards solving...

16 Carolin Benjamins, et al. ∙

∙ 02/07/2022

Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration

It has long been observed that the performance of evolutionary algorithm...

0 André Biedenkapp, et al. ∙

∙ 01/11/2022

Automated Reinforcement Learning (AutoRL): A Survey and Open Problems

The combination of Reinforcement Learning (RL) with deep learning has le...

38 Jack Parker-Holder, et al. ∙

∙ 10/05/2021

CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning

While Reinforcement Learning has made great strides towards solving ever...

0 Carolin Benjamins, et al. ∙

∙ 09/20/2021

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

Algorithm parameters, in particular hyperparameters of machine learning ...

0 Marius Lindauer, et al. ∙

∙ 06/09/2021

TempoRL: Learning When to Act

Reinforcement learning is a powerful approach to learn behaviour through...

0 André Biedenkapp, et al. ∙

∙ 06/09/2021

Self-Paced Context Evaluation for Contextual Reinforcement Learning

Reinforcement learning (RL) has made a lot of advances for solving a sin...

0 Theresa Eimer, et al. ∙

∙ 05/18/2021

DACBench: A Benchmark Library for Dynamic Algorithm Configuration

Dynamic Algorithm Configuration (DAC) aims to dynamically control a targ...

0 Theresa Eimer, et al. ∙

∙ 05/03/2021

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

Neural architecture search (NAS) and hyperparameter optimization (HPO) m...

0 Julia Guerrero-Viu, et al. ∙

∙ 02/26/2021

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

Model-based Reinforcement Learning (MBRL) is a promising framework for l...

0 Baohe Zhang, et al. ∙

∙ 02/05/2021

In-Loop Meta-Learning with Gradient-Alignment Reward

At the heart of the standard deep learning training loop is a greedy gra...

0 Samuel Müller, et al. ∙

∙ 12/15/2020

Squirrel: A Switching Hyperparameter Optimizer

In this short note, we describe our submission to the NeurIPS 2020 BBO c...

26 Noor Awad, et al. ∙

∙ 09/03/2020

Sample-Efficient Automated Deep Reinforcement Learning

Despite significant progress in challenging problems across various doma...

0 Jörg K. H. Franke, et al. ∙

∙ 06/15/2020

Learning Heuristic Selection with Dynamic Algorithm Configuration

A key challenge in satisfying planning is to use multiple heuristics wit...

0 David Speck, et al. ∙

∙ 08/19/2019

Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters

Bayesian Optimization (BO) is a common approach for hyperparameter optim...

0 Marius Lindauer, et al. ∙

∙ 08/16/2019

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

Hyperparameter optimization and neural architecture search can become pr...

0 Marius Lindauer, et al. ∙

∙ 06/18/2019

Towards White-box Benchmarks for Algorithm Control

The performance of many algorithms in the fields of hard combinatorial p...

0 André Biedenkapp, et al. ∙

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