Classification is a fundamental problem in machine learning, and considerable efforts have
been recently devoted to the demanding long-tailed setting due to its prevalence in nature …

Optimal transport (OT) is attracting increasing attention in machine learning. It aims to
transport a source distribution to a target one at minimal cost. In its vanilla form, the source …

Multi-view clustering aims to learn discriminative representations from multi-view data.
Although existing methods show impressive performance by leveraging contrastive learning …

Optimal Transport is a useful metric to compare probability distributions and to compute a
pairing given a ground cost. Its entropic regularization variant (eOT) is crucial to have fast …

Virtual Screening is an essential technique in the early phases of drug discovery, aimed at
identifying promising drug candidates from vast molecular libraries. Recently, ligand-based …

Learning meaningful representations of complex objects that can be seen through multiple
($ k\geq 3$) views or modalities is a core task in machine learning. Existing methods use …

Within the field of optimal transport (OT), the choice of ground cost is crucial to ensuring that
the optimality of a transport map corresponds to usefulness in real-world applications. It is …

Given a graph pair $(G^ 1, G^ 2) $, graph edit distance (GED) is defined as the minimum
number of edit operations converting $ G^ 1$ to $ G^ 2$. GED is a fundamental operation …

We study an online learning problem where, over $ T $ rounds, a learner observes both time-
varying sets of feasible actions and an agent's optimal actions, selected by solving linear …

The goal of optimal transport (OT) is to find optimal assignments or matchings between data
sets which minimize the total cost for a given cost function. However, sometimes the cost …