UMDA combines multi-objective learning with uncertainty modeling for RTA interception and applies distillation to enable single-pass aleatoric plus epistemic uncertainty with 10x inference speedup on JD and Criteo data.
Metabalance: improving multi-task recommendations via adapting gradient magni- tudes of auxiliary tasks,
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Uncertainty Modeling for Multi-Objective RTA Interception with Distillation Acceleration
UMDA combines multi-objective learning with uncertainty modeling for RTA interception and applies distillation to enable single-pass aleatoric plus epistemic uncertainty with 10x inference speedup on JD and Criteo data.