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.

  • [Publication] 01/2024 One paper on resource-efficient self-supervised contrastive learning accepted in ICLR 2024! We achieve competitive or SOTA results on ImageNet and other standard datasets with an impressively small batchsize. This method also has the promising downstream viability on transfer learning and few-shot learning. Code and paper will come soon! Big congratulations to Rohan and other coauthors!

  • [Award] 12/2023 Glad to receive CSE Junior Faculty Research Award from UB CSE. Thanks to the department and my students!

  • [Talk] 12/2023 Glad to visit RPI ECSE and give a talk on bilevel optimization for machine learning and beyond. Many thanks to Tianyi's invitation and host!

  • [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.

  • [New Member] 11/2023 New Ph.D. student Hao Ban (BS and MS from Southeast University, China) will join our group in Spring 2024. Welcome, Hao!

  • [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!

  • [Talk] 06/2023 Gave an invited talk on bilevel optimization and continual learning at SIAM Conference on Optimization.

  • [Award] 02/2023 Selected as Top Reviewer in AISTATS 2023.

  • [Manuscript] 01/2023 A manuscript on fair resource allocation over communication network is available online: Network Utility Maximization with Bilevel Learning. We propose a new distributed data-driven bilevel optimization approach for network utility maximization with unknown user utilities!

  • [Service] 10/2022 Will serve as TPC member of ACM MobiHoc 2023. Please consider submitting your best work to the conference!

  • [Talk] 10/2022 Gave an invited talk in the session “Bilevel Stochastic Methods for Optimization and Learning” at the 2022 INFORMS Annual Meeting, Indianapolis, IN.