Gaoyuan Zhang, Songtao Lu, et al.
UAI 2022
This paper discusses formulations and algorithms which allow a number of agents to collectively solve problems involving both (non-convex) minimization and (concave) maximization operations. These problems have a number of interesting applications in information processing and machine learning, and in particular can be used to model an adversary learning problem called network data poisoning. We develop a number of algorithms to efficiently solve these non-convex min-max optimization problems, by combining techniques such as gradient tracking in the decentralized optimization literature and gradient descent-ascent schemes in the min-max optimization literature. Also, we establish convergence to a first order stationary point under certain conditions. Finally, we perform experiments to demonstrate that the proposed algorithms are effective in the data poisoning attack.
Gaoyuan Zhang, Songtao Lu, et al.
UAI 2022
Songtao Lu, Meisam Razaviyayn, et al.
NeurIPS 2020
Leda Sari, Samuel Thomas, et al.
ICASSP 2020
Songtao Lu, Rahul Singh, et al.
ACSSC 2019