OPtimization and Trustworthy Machine Learning (OPTML) group is an active research group at Michigan State University. Our research interests span the areas of machine learning (ML)/ deep learning (DL), optimization, computer vision, security, signal processing and data science, with a focus on developing learning algorithms and theory, as well as robust and explainable artificial intelligence (AI). These research themes provide a solid foundation for reaching the long-term research objective: Making AI systems scalable and trustworthy.
As AI moves from the lab into the real world (e.g., autonomous vehicles), ensuring its safety becomes a paramount requirement prior to its deployment. Moreover, as datasets, ML/DL models, and learning tasks become increasingly complex, getting ML/DL to scale calls for new advances in learning algorithm design. More broadly, the study towards robust and scalable AI could make a significant impact on machine learning theories, and induce more promising applications in, e.g., automated ML, meta-learning, privacy and security, hardware design, and big data analysis. We seek a new learning frontier when the current learning algorithms become infeasible, and formalize foundations of secure learning.
We always look for passionate students to join the team in terms of RA/TA/externship/internship/visiting students (more info)!
Authors marked in bold indicate our group members, and “*” indicates equal contribution.
Trustworthy AI: Robustness, fairness, and model explanation
Model sparsification can simplify machine unlearning
J. Jia*, J. Liu*, P. Ram, Y. Yao, G. Liu, Y. Liu, P. Sharma, S. Liu
NeurIPS’23 (Spotlight)
Understanding and Improving Visual Prompting: A Label-Mapping Perspective
A. Chen, Y. Yao, P.-Y. Chen, Y. Zhang, S. Liu
CVPR’23
Revisiting and advancing fast adversarial training through the lens of bi-level optimization
Y. Zhang*, G. Zhang*, P. Khanduri, M. Hong, S. Chang, S. Liu
ICML’22
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
Y. Zhang, Y. Yao, J. Jia, J. Yi, M. Hong, S. Chang, S. Liu
ICLR’22 (Spotlight)
Reverse Engineering of Imperceptible Adversarial Image Perturbations
Y. Gong*, Y. Yao*, Y. Li, Y. Zhang, X. Liu, X. Lin, S. Liu
ICLR’22
Scalable AI: Model & data compression, distributed learning, black-box optimization, and automated ML
Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning
Y. Zhang*, Y. Zhang*, A. Chen, J. Jia, J. Liu, G. Liu, M. Hong, S. Chang, S. Liu
NeurIPS’23
Advancing Model Pruning via Bi-level Optimization
Y. Zhang*, Y. Yao*, P. Ram, P. Zhao, T. Chen, M. Hong, Y. Wang, S. Liu
NeurIPS’22
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
G. Zhang*, S. Lu*, Y. Zhang, X. Chen, P.-Y. Chen, Q. Fan, L. Martie, L. Horesh, M. Hong, S. Liu
UAI’22 (Best Paper Runner-Up Award)
Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML
S. Liu, S. Lu, X. Chen, Y. Feng, K. Xu, A. Al-Dujaili, M. Hong, U.-M. O’Reilly
ICML’20
A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning
S. Liu, P.-Y. Chen, B. Kailkhura, G. Zhang, A. O. Hero, P. K. Varshney
IEEE Signal Processing Magazine, 2020
We are grateful for funding from Michigan State University, MIT-IBM Watson AI Lab, DARPA, Cisco Research, NSF, DSO National Laboratories, LLNL, ARO, Amazon Research.
Six papers in NeurIPS’24, including one in dataset & benchmark track. Congrats to Yihua Zhang, Yuguang Yao, Jinghan Jia, and Yimeng Zhang for their outstanding leadership!
20 September 2024One paper in EMNLP’24: SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning
20 August 2024Grateful to receive the Amazon Research Award for AI in Information Security–Spring 2024!
20. July 2024The 3rd AdvML-Frontiers Workshop is now live and will be co-located at NeurIPS’24! Submit your papers by Aug 30.
11. July 2024Dr. Liu has received the prestigious NSF Faculty Early Career Development (CAREER) Award!
10. July 2024Congratulations to Yihua for receiving the 2024 MLCommons Rising Stars Award!
1. July 2024Two papers in ECCV’24: (1) Exploring adversarial robustness of safety-driven concept-unlearned diffusion models through a diffusion classifier perspective [Paper]; (2) Challenging forgets to unveil when and why machine unlearning could be more challenging than common beliefs [paper]
10. June 2024Two papers accepted in ICML’24: (1) Benchmarking zeroth-order optimization for memory-efficient LLM fine-tuning; (2) Why does graph transformer generalize? A Theoretical Dive into Self-attention and Positional Encoding