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Kanye Ye Wang

2 accepted papers

2026

Differentiable Semantic Meta-Learning Framework for Long-Tail Motion Forecasting in Autonomous Driving

AAAI 2026technical

Long-tail motion forecasting is a core challenge for autonomous driving, where rare yet safety-critical events-such as abrupt maneuvers and dense multi-agent interactions-dominate real-world risk. Existing approaches struggle in these scenarios because they rely on either non-interpretable clusterin

Cited by 0SourcePDFScholar
2025

Zero-shot Federated Unlearning via Transforming from Data-Dependent to Personalized Model-Centric

IJCAI 2025

Federated Unlearning (FU) addresses the "right to be forgotten" in federated learning by removing specific client data's contribution without retraining from scratch. Existing FUs are data-dependent, which make the assumption that systems can access original training data or stored historical parame

Cited by 0SourcePDFScholar