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cs.LG2025
Forgetting Any Data at Any Time: A Theoretically Certified Unlearning Framework for Vertical Federated Learning
Linian Wang, Leye Wang
Privacy concerns in machine learning are heightened by regulations such as the GDPR, which enforces the "right to be forgotten" (RTBF), driving the emergence of machine unlearning…
cs.LG2024
Revisiting Day-ahead Electricity Price: Simple Model Save Millions
Linian Wang, Jianghong Liu, Huibin Zhang +1
Accurate day-ahead electricity price forecasting is essential for residential welfare, yet current methods often fall short in forecast accuracy. We observe that commonly used time…
cs.LG2023
Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer
Liyue Chen, Linian Wang, Jinyu Xu +5
Most state-of-the-art deep domain adaptation techniques align source and target samples in a global fashion. That is, after alignment, each source sample is expected to become simi…