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20122019
most citedEdge Label Inference in Generalized Stochastic Block Models: from Spectral Theory to Impossibility Results

34 citations · 150 across the 9 of their papers we have counts for

collaborators

9 papers

math.PR201921 cited

Spectral Graph Matching and Regularized Quadratic Relaxations II: Erdős-Rényi Graphs and Universality

Zhou Fan, Cheng Mao, Yihong Wu +1

We analyze a new spectral graph matching algorithm, GRAph Matching by Pairwise eigen-Alignments (GRAMPA), for recovering the latent vertex correspondence between two unlabeled, edg…

stat.ML201919 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…

cs.IR20191 cited

POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion

Wen Chen, Pipei Huang, Jiaming Xu +7

Increasing demand for fashion recommendation raises a lot of challenges for online shopping platforms and fashion communities. In particular, there exist two requirements for fashi…

cs.NI20192 cited

Improved queue-size scaling for input-queued switches via graph factorization

Jiaming Xu, Yuan Zhong

This paper studies the scaling of the expected total queue size in an input-queued switch, as a function of both the load and the system scale . We provide a new…

math.ST201611 cited

Information-theoretic bounds and phase transitions in clustering, sparse PCA, and submatrix localization

Jess Banks, Cristopher Moore, Nicolas Verzelen +2

We study the problem of detecting a structured, low-rank signal matrix corrupted with additive Gaussian noise. This includes clustering in a Gaussian mixture model, sparse PCA, and…

math.ST201434 cited

Edge Label Inference in Generalized Stochastic Block Models: from Spectral Theory to Impossibility Results

Jiaming Xu, Laurent Massoulié, Marc Lelarge

The classical setting of community detection consists of networks exhibiting a clustered structure. To more accurately model real systems we consider a class of networks (i) whose…