4 citations · 7 across the 5 of their papers we have counts for
4 papers · 1 filter
Learning to Order: Task Sequencing as In-Context Optimization
Jan Kobiolka, Christian Frey, Arlind Kadra +2
Task sequencing (TS) is one of the core open problems in Deep Learning, arising in a plethora of real-world domains, from robotic assembly lines to autonomous driving. Unfortunatel…
POP: Prior-Fitted First-Order Optimization Policies
Jan Kobiolka, Christian Frey, Gresa Shala +3
Gradient-based optimizers are highly sensitive to design choices in their adaptive learning rate mechanisms. To address this limitation, we introduce POP, a meta-learned Reinforcem…
Hierarchical Transformers are Efficient Meta-Reinforcement Learners
Gresa Shala, André Biedenkapp, Josif Grabocka
We introduce Hierarchical Transformers for Meta-Reinforcement Learning (HTrMRL), a powerful online meta-reinforcement learning approach. HTrMRL aims to address the challenge of ena…
Squirrel: A Switching Hyperparameter Optimizer
Noor Awad, Gresa Shala, Difan Deng +9
In this short note, we describe our submission to the NeurIPS 2020 BBO challenge. Motivated by the fact that different optimizers work well on different problems, our approach swit…