Publication List
: Lifelong Learning; : Robustness; : Data-Model Efficiency; : Distributed Learning
- [MSU] Model Sparsity Can Simplify Machine Unlearning, Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu, NeurIPS 2023 (Spotlight), [].
- [MSU] Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning, Yihua Zhang, Yimeng Zhang, Aochuan Chen, Jinghan Jia, Jiancheng Liu, Gaowen Liu, Mingyi Hong, Shiyu Chang, Sijia Liu, NeurIPS 2023, [].
- [UM] Minimum-Risk Recalibration of Classifiers, Zeyu Sun, Dogyoon Song, Alfred Hero, NeurIPS 2023 (Spotlight), [].
- [MSU-UM] Robustness-Preserving Lifelong Learning Via Dataset Condensation, Jinghan Jia, Yihua Zhang, Dogyoon Song, Sijia Liu, Alfred Hero, ICASSP 2023, [, ].
- [UM] Multi-Trigger-Key: Towards Multi-Task Privacy Preserving In Deep Learning, Ren Wang, Zhe Zhu, Alfred Hero, IEEE Access, Feb 2024, []
- [UM] Securely Aggregated Coded Matrix Inversion, Neo Charalambides, Mert Pilanci, Alfred Hero, Journal of Selected Areas of Information Theory, Special Issue Dedicated to the Memory of Alex Vardi, Oct 2023, []
Project Highlights
NeurIPS 2023 [Spotlight]
Model Sparsity Can Simplify Machine Unlearning
Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu
The MSU team investigates the concept of “forgetting” (or “machine unlearning”) in ML training, specifically in the context of “targeted forgetting” to respond to data deletion tasks from users. This research demonstrates, both theoretically and practically, that “model sparsity” can effectively facilitate unlearning for better outcomes. The research on machine unlearning also links to “continual learning”.
NeurIPS 2023 [Spotlight]
Minimum-Risk Recalibration of Classifiers
Zeyu Sun, Dogyoon Song, Alfred Hero
The UM team investigates the realm of transfer learning, particularly when recalibrating a pre-trained ML model to new local data. This work provides valuable insights, demonstrating the advantages of calibrating a pre-trained model compared to training from scratch on all data, and has significant relevance in scenarios like federated learning, where models are shared among agents, one of whom possesses a large pretrained model.
NeurIPS 2023
Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning
Yihua Zhang, Yimeng Zhang, Aochuan Chen, Jinghan Jia, Jiancheng Liu, Gaowen Liu, Mingyi Hong, Shiyu Chang, Sijia Liu
The MSU team explores the efficient pruning of large-scale source datasets to facilitate more efficient source/foundational model training without compromising transfer learning performance on downstream tasks.
ICASSP 2023
Robustness-Preserving Lifelong Learning Via Dataset Condensation
Jinghan Jia, Yihua Zhang, Dogyoon Song, Sijia Liu, Alfred Hero
The MSU-UM team proposes a new memory-replay LL strategy that leverages modern bi-level optimization techniques to determine the "coreset" of the current data (i.e., a small amount of data to be memorized) for ease of preserving adversarial robustness over time.