<ul><li>Real-world event sequences often have clustering structures, but most existing temporal point processes (TPPs) ignore them.</li><li>A new study proposes learning structure-enhanced TPPs with Gromov-Wasserstein (GW) regularization.</li><li>The proposed method imposes clustering structures on TPPs for improved interpretability in modeling and prediction.</li><li>The learned TPPs demonstrate clustered sequence embeddings and competitive predictive and clustering performance.</li></ul>

Learning Structure-enhanced Temporal Point Processes with Gromov-Wasserstein Regularization

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