The Indian Buffet Hawkes Process to Model Evolving Latent Influences

Xi Tan, Vinayak Rao, Jennifer Neville
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:794-803, 2018.

Abstract

Temporal events in the real world often exhibit reinforcing dynamics, where earlier events trigger follow-up activity in the near future. A canonical example of modeling such dy- namics is the Hawkes process (HP). However, previous HP models do not capture the rich dynamics of real-world activity—which can be driven by multiple latent triggering factors shared by past and future events, with the la- tent features themselves exhibiting temporal dependency structures. For instance, rather than view a new document just as a response to other documents in the recent past, it is impor- tant to account for the factor-structure under- lying all previous documents. This structure itself is not fixed, with the influence of earlier documents decaying with time. To this end, we propose a novel Bayesian nonparametric stochastic point process model, the Indian Buf- fet Hawkes Processes (IBHP), to learn multiple latent triggering factors underlying streaming document/message data. The IBP facilitates the inclusion of multiple triggering factors in the HP, and the HP allows for modeling latent factor evolution in the IBP. We develop a learn- ing algorithm for the IBHP based on Sequen- tial Monte Carlo and demonstrate the effective- ness of the model. In both synthetic and real data experiments, our model achieves equiv- alent or higher likelihood and provides inter- pretable topics and shows their dynamics.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-tan18b, title = {The Indian Buffet Hawkes Process to Model Evolving Latent Influences}, author = {Tan, Xi and Rao, Vinayak and Neville, Jennifer}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {794--803}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/tan18b/tan18b.pdf}, url = {https://proceedings.mlr.press/r16/tan18b.html}, abstract = {Temporal events in the real world often exhibit reinforcing dynamics, where earlier events trigger follow-up activity in the near future. A canonical example of modeling such dy- namics is the Hawkes process (HP). However, previous HP models do not capture the rich dynamics of real-world activity—which can be driven by multiple latent triggering factors shared by past and future events, with the la- tent features themselves exhibiting temporal dependency structures. For instance, rather than view a new document just as a response to other documents in the recent past, it is impor- tant to account for the factor-structure under- lying all previous documents. This structure itself is not fixed, with the influence of earlier documents decaying with time. To this end, we propose a novel Bayesian nonparametric stochastic point process model, the Indian Buf- fet Hawkes Processes (IBHP), to learn multiple latent triggering factors underlying streaming document/message data. The IBP facilitates the inclusion of multiple triggering factors in the HP, and the HP allows for modeling latent factor evolution in the IBP. We develop a learn- ing algorithm for the IBHP based on Sequen- tial Monte Carlo and demonstrate the effective- ness of the model. In both synthetic and real data experiments, our model achieves equiv- alent or higher likelihood and provides inter- pretable topics and shows their dynamics.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T The Indian Buffet Hawkes Process to Model Evolving Latent Influences %A Xi Tan %A Vinayak Rao %A Jennifer Neville %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-tan18b %I PMLR %P 794--803 %U https://proceedings.mlr.press/r16/tan18b.html %V R16 %X Temporal events in the real world often exhibit reinforcing dynamics, where earlier events trigger follow-up activity in the near future. A canonical example of modeling such dy- namics is the Hawkes process (HP). However, previous HP models do not capture the rich dynamics of real-world activity—which can be driven by multiple latent triggering factors shared by past and future events, with the la- tent features themselves exhibiting temporal dependency structures. For instance, rather than view a new document just as a response to other documents in the recent past, it is impor- tant to account for the factor-structure under- lying all previous documents. This structure itself is not fixed, with the influence of earlier documents decaying with time. To this end, we propose a novel Bayesian nonparametric stochastic point process model, the Indian Buf- fet Hawkes Processes (IBHP), to learn multiple latent triggering factors underlying streaming document/message data. The IBP facilitates the inclusion of multiple triggering factors in the HP, and the HP allows for modeling latent factor evolution in the IBP. We develop a learn- ing algorithm for the IBHP based on Sequen- tial Monte Carlo and demonstrate the effective- ness of the model. In both synthetic and real data experiments, our model achieves equiv- alent or higher likelihood and provides inter- pretable topics and shows their dynamics. %Z Reissued by PMLR on 04 October 2026.
APA
Tan, X., Rao, V. & Neville, J.. (2018). The Indian Buffet Hawkes Process to Model Evolving Latent Influences. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:794-803 Available from https://proceedings.mlr.press/r16/tan18b.html. Reissued by PMLR on 04 October 2026.

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