91 citations · 129 across the 16 of their papers we have counts for
4 papers · 1 filter
Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network
Theresa Eimer, Lennart Schäpermeier, André Biedenkapp +15
Empirical research on meta-algorithmics, such as algorithm selection, configuration, and scheduling, often relies on extensive and thus computationally expensive experiments. With…
Revisiting Learning Rate Control
Micha Henheik, Theresa Eimer, Marius Lindauer
The learning rate is one of the most important hyperparameters in deep learning, and how to control it is an active area within both AutoML and deep learning research. Approaches f…
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…
Task Scheduling & Forgetting in Multi-Task Reinforcement Learning
Marc Speckmann, Theresa Eimer
Reinforcement learning (RL) agents can forget tasks they have previously been trained on. There is a rich body of work on such forgetting effects in humans. Therefore we look for c…