Assistant Professor, Eindhoven University of Technology
5 papers at NeurIPS 2025
We propose UniteFormer, a unified neural solver that supports node-only, edge-only, and hybrid input types through a single model trained via joint edge-node modalities.
This paper proposes the Multi-Task Learning with Knowledge Distillation (MTL-KD) to train a single heavy decoder model without labeled data to solve various VRP variants.