Carolin Benjamins | DeepAI

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∙ 06/07/2023

Self-Adjusting Weighted Expected Improvement for Bayesian Optimization

Bayesian Optimization (BO) is a class of surrogate-based, sample-efficie...

0 Carolin Benjamins, et al. ∙

∙ 04/05/2023

AutoRL Hyperparameter Landscapes

Although Reinforcement Learning (RL) has shown to be capable of producin...

0 Aditya Mohan, et al. ∙

∙ 12/21/2022

Hyperparameters in Contextual RL are Highly Situational

Although Reinforcement Learning (RL) has shown impressive results in gam...

0 Theresa Eimer, et al. ∙

∙ 11/17/2022

Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis

Bayesian optimization (BO) algorithms form a class of surrogate-based he...

0 Carolin Benjamins, et al. ∙

∙ 11/02/2022

PI is back! Switching Acquisition Functions in Bayesian Optimization

Bayesian Optimization (BO) is a powerful, sample-efficient technique to ...

0 Carolin Benjamins, et al. ∙

∙ 05/23/2022

POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning

The goal of Unsupervised Reinforcement Learning (URL) is to find a rewar...

11 Frederik Schubert, 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. ∙

∙ 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. ∙

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