Abstract

Copious sequential event data has consistently increased in various high-impact domains such as social media and sharing economy. When events start to take place in a sequential fashion, an important question arises: “what type of event will happen at what time in the near future?” To answer the question, a class of mathematical models called the marked temporal point process is often exploited as it can model the timing and properties of events seamlessly in a joint framework. Recently, various recurrent neural network (RNN) models are proposed to enhance the predictive power of mark temporal point process. However, existing marked temporal point models are fundamentally based on the Maximum Likelihood Estimation (MLE) framework for the training, and inevitably suffer from the problem resulted from the intractable likelihood function. Surprisingly, little attention has been paid to address this issue. In this work, we propose INITIATOR - a novel training framework based on noise-contrastive estimation to resolve this problem. Theoretically, we show the exists a strong connection between the proposed INITIATOR and the exact MLE. Experimentally, the efficacy of INITIATOR is demonstrated over the state-of-the-art approaches on several real-world datasets from various areas.

Original languageEnglish (US)
Title of host publicationProceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
EditorsJerome Lang
PublisherInternational Joint Conferences on Artificial Intelligence
Pages2191-2197
Number of pages7
Volume2018-July
ISBN (Electronic)9780999241127
StatePublished - Jan 1 2018
Event27th International Joint Conference on Artificial Intelligence, IJCAI 2018 - Stockholm, Sweden
Duration: Jul 13 2018Jul 19 2018

Other

Other27th International Joint Conference on Artificial Intelligence, IJCAI 2018
CountrySweden
CityStockholm
Period7/13/187/19/18

Fingerprint

Maximum likelihood estimation
Recurrent neural networks
Mathematical models

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Guo, R., Li, J., & Liu, H. (2018). Initiator: Noise-contrastive estimation for marked temporal point process. In J. Lang (Ed.), Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018 (Vol. 2018-July, pp. 2191-2197). International Joint Conferences on Artificial Intelligence.

Initiator : Noise-contrastive estimation for marked temporal point process. / Guo, Ruocheng; Li, Jundong; Liu, Huan.

Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018. ed. / Jerome Lang. Vol. 2018-July International Joint Conferences on Artificial Intelligence, 2018. p. 2191-2197.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Guo, R, Li, J & Liu, H 2018, Initiator: Noise-contrastive estimation for marked temporal point process. in J Lang (ed.), Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018. vol. 2018-July, International Joint Conferences on Artificial Intelligence, pp. 2191-2197, 27th International Joint Conference on Artificial Intelligence, IJCAI 2018, Stockholm, Sweden, 7/13/18.
Guo R, Li J, Liu H. Initiator: Noise-contrastive estimation for marked temporal point process. In Lang J, editor, Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018. Vol. 2018-July. International Joint Conferences on Artificial Intelligence. 2018. p. 2191-2197
Guo, Ruocheng ; Li, Jundong ; Liu, Huan. / Initiator : Noise-contrastive estimation for marked temporal point process. Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018. editor / Jerome Lang. Vol. 2018-July International Joint Conferences on Artificial Intelligence, 2018. pp. 2191-2197
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