Activity Number:
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286
- Learning Networks from Point Processes: Neuronal Connectivity Networks and Beyond
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Type:
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Invited
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #309322
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Title:
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Context-Dependent Self-Exciting Point Processes: Models, Methods, and Risk Bounds in High Dimensions
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Author(s):
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Garvesh Raskutti* and Lili Zheng and Rebecca Willett
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Companies:
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UW-Madison and University of Wisconsin-Madison and University of Chicago
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Keywords:
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point process;
network
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Abstract:
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High-dimensional autoregressive point processes model how current events can trigger or inhibit future events, such as activity in a social network. Specifically, we leverage ideas from compositional data analysis and regularization tools for machine learning to conduct network estimation for high-dimensional marked point processes. Two models and corresponding estimators are considered in detail: an autoregressive multinomial model suited to noise-free categorical contexts and a logistic-normal model suited to events with mixed membership in different categories and noisy observations. Importantly, the logistic-normal model leads to a convex negative log-likelihood objective and captures dependence across categories. We provide theoretical guarantees for both estimators and demonstrate through simulations and three real data examples the advantages and disadvantages of both approaches.
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Authors who are presenting talks have a * after their name.
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