Papers Published

1. Xu, H; Luo, D; Carin, L, Online continuous-time tensor factorization based on pairwise interactive point processes, IJCAI International Joint Conference on Artificial Intelligence, vol. 2018-July (January, 2018), pp. 2905-2911 [doi] .
   (last updated on 2024/12/31)

   Abstract:
   A continuous-time tensor factorization method is developed for event sequences containing multiple “modalities.” Each data element is a point in a tensor, whose dimensions are associated with the discrete alphabet of the modalities. Each tensor data element has an associated time of occurence and a feature vector. We model such data based on pairwise interactive point processes, and the proposed framework connects pairwise tensor factorization with a feature-embedded point process. The model accounts for interactions within each modality, interactions across different modalities, and continuous-time dynamics of the interactions. Model learning is formulated as a convex optimization problem, based on online alternating direction method of multipliers. Compared to existing state-of-the-art methods, our approach captures the latent structure of the tensor and its evolution over time, obtaining superior results on real-world datasets.