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
Understanding and Improving Visual Prompting: A Label-Mapping Perspective
A. Chen, Y. Yao, P.-Y. Chen, Y. Zhang, S. Liu
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
Reverse Engineering of Imperceptible Adversarial Image Perturbations
Y. Gong*, Y. Yao*, Y. Li, Y. Zhang, X. Liu, X. Lin, S. Liu
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
Proper Network Interpretability Helps Adversarial Robustness in Classification
A. Boopathy, S. Liu, G. Zhang, C. Liu, P.-Y. Chen, S. Chang, L. Daniel
Scalable AI: Model compression, distributed learning, black-box optimization, and automated ML
Advancing Model Pruning via Bi-level Optimization
Y. Zhang*, Y. Yao*, P. Ram, P. Zhao, T. Chen, M. Hong, Y. Wang, S. Liu
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
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
An ADMM Based Framework for AutoML Pipeline Configuration
S. Liu*, P. Ram*, D. Vijaykeerthy, D. Bouneffouf, G. Bramble, H. Samulowitz, D. Wang, A. Conn, A. Gray,
We are grateful for funding from Michigan State University, MIT-IBM Watson AI Lab, DARPA, Cisco Research, NSF, DSO National Laboratories, and LLNL.
Our paper Visual Prompting for Adversarial Robustness received the Top 3% Paper Recognition at ICASSP 2023. Congrats to Aochuan, Peter (internship at OPTML in 2022), Yuguang, and Pin-Yu (IBM Research)!24. April 2023
Two papers in ICML’23 and CFP for 2nd AdvML-Frontiers Workshop @ICML’23.17. April 2023
A new arXiv paper is released: Model Sparsification Can Simplify Machine Unlearning (see paper and code)!13. April 2023
Grateful to receive a grant from Lawrence Livermore National Laboratory.1. April 2023
Call for Papers and AdvML Rising Star Award Applications in the workshop AdvML-Frontiers, ICML’2317. March 2023
A new arXiv paper is released: Adversarial attacks can be parsed to reveal victim model information! (see [Paper])17. March 2023
The 2nd Workshop on New Frontiers in Adversarial Machine Learning has been accepted by ICML’231. March 2023
Grateful to receive a grant from DSO National Laboratories.