21 citations · 98 across the 14 of their papers we have counts for
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stat.ML2019★ 19 cited
Spectral Graph Matching and Regularized Quadratic Relaxations I: The Gaussian Model
Zhou Fan, Cheng Mao, Yihong Wu +1
Graph matching aims at finding the vertex correspondence between two unlabeled graphs that maximizes the total edge weight correlation. This amounts to solving a computationally in…
stat.ML2019★ 5 cited
Data Amplification: A Unified and Competitive Approach to Property Estimation
Yi Hao, Alon Orlitsky, Ananda T. Suresh +1
Estimating properties of discrete distributions is a fundamental problem in statistical learning. We design the first unified, linear-time, competitive, property estimator that for…
stat.ML2018
Efficient random graph matching via degree profiles
Jian Ding, Zongming Ma, Yihong Wu +1
Random graph matching refers to recovering the underlying vertex correspondence between two random graphs with correlated edges; a prominent example is when the two random graphs a…