30 citations · 37 across the 11 of their papers we have counts for
5 papers · 1 filter
Discovering Minimal Reinforcement Learning Environments
Jarek Liesen, Chris Lu, Andrei Lupu +3
Reinforcement learning (RL) agents are commonly trained and evaluated in the same environment. In contrast, humans often train in a specialized environment before being evaluated,…
Boosting Fairness and Robustness in Over-the-Air Federated Learning
Halil Yigit Oksuz, Fabio Molinari, Henning Sprekeler +1
Over-the-Air Computation is a beyond-5G communication strategy that has recently been shown to be useful for the decentralized training of machine learning models due to its effici…
Federated Learning in Wireless Networks via Over-the-Air Computations
Halil Yigit Oksuz, Fabio Molinari, Henning Sprekeler +1
In a multi-agent system, agents can cooperatively learn a model from data by exchanging their estimated model parameters, without the need to exchange the locally available data us…
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning
Marc Aurel Vischer, Robert Tjarko Lange, Henning Sprekeler
The lottery ticket hypothesis questions the role of overparameterization in supervised deep learning. But how is the performance of winning lottery tickets affected by the distribu…
Learning Not to Learn: Nature versus Nurture in Silico
Robert Tjarko Lange, Henning Sprekeler
Animals are equipped with a rich innate repertoire of sensory, behavioral and motor skills, which allows them to interact with the world immediately after birth. At the same time,…