Embedding & MLP has become a paradigm for modern large-scale recommendation
system. However, this paradigm suffers from the cold-start problem which will seriously …

Answering complex queries on knowledge graphs is important but particularly challenging
because of the data incompleteness. Query embedding methods address this issue by …

Sinkhorn algorithm has been used pervasively to approximate the solution to optimal
transport (OT) and unbalanced optimal transport (UOT) problems. However, its practical …

Softassign is a pivotal method in graph matching and other learning tasks. Many softassign-
based algorithms exhibit performance sensitivity to a parameter in the softassign. However …

As a valid metric of metric-measure spaces, Gromov-Wasserstein (GW) distance has shown
the potential for matching problems of structured data like point clouds and graphs …

Distribution comparison plays a central role in many machine learning tasks like data
classification and generative modeling. In this study, we propose a novel metric, called …

In our previous work (Liao et al. in Commun Math Sci, 2022), the complexity of Sinkhorn
iteration is reduced from O (N 2) to the optimal O (N) by leveraging the special structure of …

We shall use the Euler-Maruyama (EM) scheme with decreasing step size
$\Lambda=(\eta_n) _ {n\in\mathbb {N}} $ to approximate the steady state for piecewise …

The interplay between cognition and gaming, notably through educational games
enhancing cognitive skills, has garnered significant attention in recent years. This research …

The Gromov-Wasserstein distance is a notable extension of optimal transport. In contrast to
the classic Wasserstein distance, it solves a quadratic assignment problem that minimizes …