Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling

Hongyi Ding, Mohammad Khan, Issei Sato, Masashi Sugiyama
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR 84:1108-1116, 2018.

Abstract

Analyzing the underlying structure of multiple time-sequences provides insights into the understanding of social networks and human activities. In this work, we present the Bayesian nonparametric Poisson process allocation (BaNPPA), a latent-function model for time-sequences, which automatically infers the number of latent functions. We model the intensity of each sequence as an infinite mixture of latent functions, each of which is obtained using a function drawn from a Gaussian process. We show that a technical challenge for the inference of such mixture models is the unidentifiability of the weights of the latent functions. We propose to cope with the issue by regulating the volume of each latent function within a variational inference algorithm. Our algorithm is computationally efficient and scales well to large data sets. We demonstrate the usefulness of our proposed model through experiments on both synthetic and real-world data sets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v84-ding18a, title = {Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling}, author = {Ding, Hongyi and Khan, Mohammad and Sato, Issei and Sugiyama, Masashi}, booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics}, pages = {1108--1116}, year = {2018}, editor = {Storkey, Amos and Perez-Cruz, Fernando}, volume = {84}, series = {Proceedings of Machine Learning Research}, month = {09--11 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v84/ding18a/ding18a.pdf}, url = {https://proceedings.mlr.press/v84/ding18a.html}, abstract = {Analyzing the underlying structure of multiple time-sequences provides insights into the understanding of social networks and human activities. In this work, we present the Bayesian nonparametric Poisson process allocation (BaNPPA), a latent-function model for time-sequences, which automatically infers the number of latent functions. We model the intensity of each sequence as an infinite mixture of latent functions, each of which is obtained using a function drawn from a Gaussian process. We show that a technical challenge for the inference of such mixture models is the unidentifiability of the weights of the latent functions. We propose to cope with the issue by regulating the volume of each latent function within a variational inference algorithm. Our algorithm is computationally efficient and scales well to large data sets. We demonstrate the usefulness of our proposed model through experiments on both synthetic and real-world data sets.} }
Endnote
%0 Conference Paper %T Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling %A Hongyi Ding %A Mohammad Khan %A Issei Sato %A Masashi Sugiyama %B Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2018 %E Amos Storkey %E Fernando Perez-Cruz %F pmlr-v84-ding18a %I PMLR %P 1108--1116 %U https://proceedings.mlr.press/v84/ding18a.html %V 84 %X Analyzing the underlying structure of multiple time-sequences provides insights into the understanding of social networks and human activities. In this work, we present the Bayesian nonparametric Poisson process allocation (BaNPPA), a latent-function model for time-sequences, which automatically infers the number of latent functions. We model the intensity of each sequence as an infinite mixture of latent functions, each of which is obtained using a function drawn from a Gaussian process. We show that a technical challenge for the inference of such mixture models is the unidentifiability of the weights of the latent functions. We propose to cope with the issue by regulating the volume of each latent function within a variational inference algorithm. Our algorithm is computationally efficient and scales well to large data sets. We demonstrate the usefulness of our proposed model through experiments on both synthetic and real-world data sets.
APA
Ding, H., Khan, M., Sato, I. & Sugiyama, M.. (2018). Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 84:1108-1116 Available from https://proceedings.mlr.press/v84/ding18a.html.

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