1 citations · 1 across the 7 of their papers we have counts for
7 papers
Environment-Grounded Automated Prompt Optimization for LLM Game Agents
Rean Clive Fernandes, Lukas Fehring, Theresa Eimer +2
LLM agents in interactive environments are highly sensitive to their prompts, yet prompt engineering remains a manual, task-specific process. We introduce an automated prompt optim…
Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization
Leona Hennig, Jasmin Brandt, Lukas Fehring +3
Large-scale hyperparameter optimization (HPO) in automated machine learning (AutoML) consumes substantial computational resources, raising growing concerns about scalability and en…
Neural Attention Search Linear: Towards Adaptive Token-Level Hybrid Attention Models
Difan Deng, Andreas Bentzen Winje, Lukas Fehring +1
The quadratic computational complexity of softmax transformers has become a bottleneck in long-context scenarios. In contrast, linear attention model families provide a promising d…
Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization
Lukas Fehring, Marcel Wever, Maximilian Spliethöver +3
Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO). However, most existing HPO methods only incorporate expert knowledge during initializatio…
Growing with Experience: Growing Neural Networks in Deep Reinforcement Learning
Lukas Fehring, Marius Lindauer, Theresa Eimer
While increasingly large models have revolutionized much of the machine learning landscape, training even mid-sized networks for Reinforcement Learning (RL) is still proving to be…
PyExperimenter: Easily distribute experiments and track results
Tanja Tornede, Alexander Tornede, Lukas Fehring +5
PyExperimenter is a tool to facilitate the setup, documentation, execution, and subsequent evaluation of results from an empirical study of algorithms and in particular is designed…
HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection
Lukas Fehring, Jonas Hanselle, Alexander Tornede
It is well known that different algorithms perform differently well on an instance of an algorithmic problem, motivating algorithm selection (AS): Given an instance of an algorithm…