15 citations · 16 across the 5 of their papers we have counts for
6 papers · 1 filter
Many-to-Many Matching via Sparsity Controlled Optimal Transport
Weijie Liu, Han Bao, Makoto Yamada +3
Many-to-many matching seeks to match multiple points in one set and multiple points in another set, which is a basis for a wide range of data mining problems. It can be naturally r…
Towards Optimal Randomized Strategies in Adversarial Example Game
Jiahao Xie, Chao Zhang, Weijie Liu +2
The vulnerability of deep neural network models to adversarial example attacks is a practical challenge in many artificial intelligence applications. A recent line of work shows th…
SIGMA: A Structural Inconsistency Reducing Graph Matching Algorithm
Weijie Liu, Chao Zhang, Nenggan Zheng +1
Graph matching finds the correspondence of nodes across two correlated graphs and lies at the core of many applications. When graph side information is not available, the node corr…
Approximating Optimal Transport via Low-rank and Sparse Factorization
Weijie Liu, Chao Zhang, Nenggan Zheng +1
Optimal transport (OT) naturally arises in a wide range of machine learning applications but may often become the computational bottleneck. Recently, one line of works propose to s…
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems
Jiahao Xie, Chao Zhang, Zebang Shen +2
Minimax problems arise in a wide range of important applications including robust adversarial learning and Generative Adversarial Network (GAN) training. Recently, algorithms for m…
From One to All: Learning to Match Heterogeneous and Partially Overlapped Graphs
Weijie Liu, Hui Qian, Chao Zhang +3
Recent years have witnessed a flurry of research activity in graph matching, which aims at finding the correspondence of nodes across two graphs and lies at the heart of many artif…