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Taming the Beast of the Preimage in Machine Learning for Structured Data: Signal, Image and Graph

Generate a "median" molecule by solving the pre-image problem

Learning Output Embeddings in Structured Prediction

Spectral-designed Depthwise Separable Graph Neural Networks (ICML'20 - GRL+)

When Spectral Domain Meets Spatial Domain in Graph Neural Networks (in ICML’20 – GRL+)

Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses (ICML’20)

Analyzing the expressive power of graph neural networks in a spectral perspective (ICLR'21)

Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters (ICML'22)

Vector-Valued Least-Squares Regression under Output Regularity Assumptions (JMLR'22)

Graph Normalizing Flows to Pre-image Free Machine Learning for Regression (IAPR-TC15, 2023)

Bridging Distinct Spaces in Graph-based Machine Learning (ACPR'23)

Interpretable time series kernel analytics by pre-image estimation (Artificial Intelligence, 2020)