A derivative-free coreset task-selection algorithm for MAML-RL trains on a small weighted task subset and provably reduces sample complexity by O(1/epsilon), provided the task-selection bias is small.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.OC 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning
A derivative-free coreset task-selection algorithm for MAML-RL trains on a small weighted task subset and provably reduces sample complexity by O(1/epsilon), provided the task-selection bias is small.