2020/11/06 by Alex Boyd, Robert Bamler, Boyd, Alex +5 · 4 citations
Computer Science · Health Professions · Mathematics · Social Sciences · #Artificial intelligence #Artificial neural network #Computer science #Data mining #Data modeling #Data type #Database #Event (particle physics) #FOS: Computer and information sciences #Hidden Markov model #Human Mobility and Location-Based Analysis #Identification (biology) #Inference #Latent variable #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #Machine learning #Older Adults Driving Studies #Ranking (information retrieval) #Sequence (biology) #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2011.03231
published in arXiv (Cornell University) 33, 21488-21499 (Cornell University) · Accepted at NeurIPS 2020
arxiv created 2020/11/06 · openalex publication_date 2020/11/06 · arxiv updated 2020/11/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/09
Continuous-time event data are common in applications such as individual behavior data, financial transactions, and medical health records. Modeling such data can be very challenging, in particular for applications with many different types of events, since it requires a model to predict the event types as well as the time of occurrence. Recurrent neural networks that parameterize time-varying intensity functions are the current state-of-the-art for predictive modeling with such data. These models typically assume that all event sequences come from the same data distribution. However, in many applications event sequences are generated by different sources, or users, and their characteristics can be very different. In this paper, we extend the broad class of neural marked point process models to mixtures of latent embeddings, where each mixture component models the characteristic traits of a given user. Our approach relies on augmenting these models with a latent variable that encodes user characteristics, represented by a mixture model over user behavior that is trained via amortized variational inference. We evaluate our methods on four large real-world datasets and demonstrate systematic improvements from our approach over existing work for a variety of predictive metrics such as log-likelihood, next event ranking, and source-of-sequence identification.