News
[Manuscript] 02/2024 One manuscript “Fair Resource Allocation in Multi-Task Learning” is available online. We connect fair resource allocation in wireless communication with multi-task learning, and propose an optimization method named FairGrad. This method implements different ideas of fairness and achieves SOTA performance among gradient manipulation MTL methods with performance guarantee. The idea has also been incorporated into existing MTL methods with significant improvements observed. Check our codes: Click.
[Manuscript] 02/2024 One manuscript “Discriminative Adversarial Unlearning” is available online. We introduce a novel machine unlearning framework founded on an attacker network and a defender network, where the attacker teases out the information of the data to be unlearned, and the defender unlearns to defend the network against the attack. We also incorporate a self-supervised objective to address the feature space discrepancies between the forget and validation sets. This method closely approximates the ideal benchmark of retraining from scratch in various scenarios. Code is available at Click.
[Talk] 10,11/2023 Glad to give multiple invited talks at INFORMS 2023 (Phoenix), Asilomar 2023 (Pacific Grove), MobiHoc 2023 (Washionton DC) about our recent progress on bilevel optimization for continual learning and network resource allocation.
[Publication] 09/2023 Five papers accepted in NeurIPS 2023 with one spotlight presentation! The topics span over Hessian-free bilevel optimization, federated learning, continual learning and multi-objective learning. Big congratulations to my students Yifan, Peiyao and Hao, and many thanks to my collaborators!
|