3 citations · 14 across the 15 of their papers we have counts for
4 papers · 1 filter
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna +70
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learnin…
Self-supervised Learning for Anomaly Detection in Computational Workflows
Hongwei Jin, Krishnan Raghavan, George Papadimitriou +4
Anomaly detection is the task of identifying abnormal behavior of a system. Anomaly detection in computational workflows is of special interest because of its wide implications in…
Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound
Hongwei Jin, Zishun Yu, Xinhua Zhang
Comparing structured data from possibly different metric-measure spaces is a fundamental task in machine learning, with applications in, e.g., graph classification. The Gromov-Wass…
Gromov-Wasserstein Discrepancy with Local Differential Privacy for Distributed Structural Graphs
Hongwei Jin, Xun Chen
Learning the similarity between structured data, especially the graphs, is one of the essential problems. Besides the approach like graph kernels, Gromov-Wasserstein (GW) distance…