73 citations · 499 across the 29 of their papers we have counts for
31 papers
On the Fundamental Limits of Matrix Completion: Leveraging Hierarchical Similarity Graphs
Junhyung Ahn, Adel Elmahdy, Soheil Mohajer +1
We study the matrix completion problem that leverages hierarchical similarity graphs as side information in the context of recommender systems. Under a hierarchical stochastic bloc…
Wasserstein GAN Can Perform PCA
Jaewoong Cho, Changho Suh
Generative Adversarial Networks (GANs) have become a powerful framework to learn generative models that arise across a wide variety of domains. While there has been a recent surge…
Two-way Function Computation
Seiyun Shin, Changho Suh
We explore the role of interaction for the problem of reliable computation over two-way multicast networks. Specifically we consider a four-node network in which two nodes wish to…
Hypergraph Spectral Clustering in the Weighted Stochastic Block Model
Kwangjun Ahn, Kangwook Lee, Changho Suh
Spectral clustering is a celebrated algorithm that partitions objects based on pairwise similarity information. While this approach has been successfully applied to a variety of do…
Hierarchical Coding for Distributed Computing
Hyegyeong Park, Kangwook Lee, Jy-yong Sohn +2
Coding for distributed computing supports low-latency computation by relieving the burden of straggling workers. While most existing works assume a simple master-worker model, we c…
Understanding GANs: the LQG Setting
Soheil Feizi, Farzan Farnia, Tony Ginart +1
Generative Adversarial Networks (GANs) have become a popular method to learn a probability model from data. In this paper, we aim to provide an understanding of some of the basic i…