Probabilistic Modeling for Sequences of Sets in Continuous-Time

Yuxin Chang, Alex J Boyd, Padhraic Smyth
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:4357-4365, 2024.

Abstract

Neural marked temporal point processes have been a valuable addition to the existing toolbox of statistical parametric models for continuous-time event data. These models are useful for sequences where each event is associated with a single item (a single type of event or a “mark”)—but such models are not suited for the practical situation where each event is associated with a set of items. In this work, we develop a general framework for modeling set-valued data in continuous-time, compatible with any intensity-based recurrent neural point process model. In addition, we develop inference methods that can use such models to answer probabilistic queries such as “the probability of item A being observed before item B,” conditioned on sequence history. Computing exact answers for such queries is generally intractable for neural models due to both the continuous-time nature of the problem setting and the combinatorially-large space of potential outcomes for each event. To address this, we develop a class of importance sampling methods for querying with set-based sequences and demonstrate orders-of-magnitude improvements in efficiency over direct sampling via systematic experiments with four real-world datasets. We also illustrate how to use this framework to perform model selection using likelihoods that do not involve one-step-ahead prediction.

Cite this Paper

BibTeX
@InProceedings{pmlr-v238-chang24a, title = {Probabilistic Modeling for Sequences of Sets in Continuous-Time}, author = {Chang, Yuxin and J Boyd, Alex and Smyth, Padhraic}, booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics}, pages = {4357--4365}, year = {2024}, editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}, volume = {238}, series = {Proceedings of Machine Learning Research}, month = {02--04 May}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v238/chang24a/chang24a.pdf}, url = {https://proceedings.mlr.press/v238/chang24a.html}, abstract = {Neural marked temporal point processes have been a valuable addition to the existing toolbox of statistical parametric models for continuous-time event data. These models are useful for sequences where each event is associated with a single item (a single type of event or a “mark”)—but such models are not suited for the practical situation where each event is associated with a set of items. In this work, we develop a general framework for modeling set-valued data in continuous-time, compatible with any intensity-based recurrent neural point process model. In addition, we develop inference methods that can use such models to answer probabilistic queries such as “the probability of item A being observed before item B,” conditioned on sequence history. Computing exact answers for such queries is generally intractable for neural models due to both the continuous-time nature of the problem setting and the combinatorially-large space of potential outcomes for each event. To address this, we develop a class of importance sampling methods for querying with set-based sequences and demonstrate orders-of-magnitude improvements in efficiency over direct sampling via systematic experiments with four real-world datasets. We also illustrate how to use this framework to perform model selection using likelihoods that do not involve one-step-ahead prediction.} }
Endnote
%0 Conference Paper %T Probabilistic Modeling for Sequences of Sets in Continuous-Time %A Yuxin Chang %A Alex J Boyd %A Padhraic Smyth %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-chang24a %I PMLR %P 4357--4365 %U https://proceedings.mlr.press/v238/chang24a.html %V 238 %X Neural marked temporal point processes have been a valuable addition to the existing toolbox of statistical parametric models for continuous-time event data. These models are useful for sequences where each event is associated with a single item (a single type of event or a “mark”)—but such models are not suited for the practical situation where each event is associated with a set of items. In this work, we develop a general framework for modeling set-valued data in continuous-time, compatible with any intensity-based recurrent neural point process model. In addition, we develop inference methods that can use such models to answer probabilistic queries such as “the probability of item A being observed before item B,” conditioned on sequence history. Computing exact answers for such queries is generally intractable for neural models due to both the continuous-time nature of the problem setting and the combinatorially-large space of potential outcomes for each event. To address this, we develop a class of importance sampling methods for querying with set-based sequences and demonstrate orders-of-magnitude improvements in efficiency over direct sampling via systematic experiments with four real-world datasets. We also illustrate how to use this framework to perform model selection using likelihoods that do not involve one-step-ahead prediction.
APA
Chang, Y., J Boyd, A. & Smyth, P.. (2024). Probabilistic Modeling for Sequences of Sets in Continuous-Time. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:4357-4365 Available from https://proceedings.mlr.press/v238/chang24a.html.

Related Material