48 citations · 91 across the 3 of their papers we have counts for
7 papers
HNHN: Hypergraph Networks with Hyperedge Neurons
Yihe Dong, Will Sawin, Yoshua Bengio
Hypergraphs provide a natural representation for many real world datasets. We propose a novel framework, HNHN, for hypergraph representation learning. HNHN is a hypergraph convolut…
A Study of Performance of Optimal Transport
Yihe Dong, Yu Gao, Richard Peng +2
We investigate the problem of efficiently computing optimal transport (OT) distances, which is equivalent to the node-capacitated minimum cost maximum flow problem in a bipartite g…
COPT: Coordinated Optimal Transport for Graph Sketching
Yihe Dong, Will Sawin
We introduce COPT, a novel distance metric between graphs defined via an optimization routine, computing a coordinated pair of optimal transport maps simultaneously. This gives an…
Scalable Nearest Neighbor Search for Optimal Transport
Arturs Backurs, Yihe Dong, Piotr Indyk +2
The Optimal Transport (a.k.a. Wasserstein) distance is an increasingly popular similarity measure for rich data domains, such as images or text documents. This raises the necessity…
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
Yihe Dong, Samuel B. Hopkins, Jerry Li
We study two problems in high-dimensional robust statistics: \emph{robust mean estimation} and \emph{outlier detection}. In robust mean estimation the goal is to estimate the mean…
SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search
Hao Chen, Ilaria Chillotti, Yihe Dong +3
The -Nearest Neighbor Search (-NNS) is the backbone of several cloud-based services such as recommender systems, face recognition, and database search on text and images. In…