Decentralized learning has recently been attracting increasing attention...
Graph Neural Networks (GNNs) are popular models for graph learning probl...
Labeling data is one of the most costly processes in machine learning
pi...
SGD with momentum acceleration is one of the key components for improvin...
Traditionally, recommendation algorithms have been designed for service
...
The research process includes many decisions, e.g., how to entitle and w...
Wasserstein distance, which measures the discrepancy between distributio...
We use many search engines on the Internet in our daily lives. However, ...
Word embeddings are one of the most fundamental technologies used in nat...
Suppose we have a black-box function (e.g., deep neural network) that ta...
The Wasserstein barycenter has been widely studied in various fields,
in...
In package recommendations, a set of items is regarded as a unified pack...
The word mover's distance (WMD) is a fundamental technique for measuring...
Fairness is an important property in data-mining applications, including...
To measure the similarity of documents, the Wasserstein distance is a
po...
Choosing a publication venue for an academic paper is a crucial step in ...
This study examines the time complexities of the unbalanced optimal tran...
Graph neural networks (GNNs) are effective machine learning models for
v...
Graph neural networks (GNNs) are powerful machine learning models for va...
The problem of comparing distributions endowed with their own geometry
a...
In this paper, from a theoretical perspective, we study how powerful gra...
Finding hard instances, which need a long time to solve, of graph proble...
Recent advancements in graph neural networks (GNN) have led to
state-of-...