Search Results for author: Benjamin Mark

Found 4 papers, 0 papers with code

Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions

no code implementations16 Mar 2020 Lili Zheng, Garvesh Raskutti, Rebecca Willett, Benjamin Mark

High-dimensional autoregressive point processes model how current events trigger or inhibit future events, such as activity by one member of a social network can affect the future activity of his or her neighbors.

Point Processes Time Series

Estimating Network Structure from Incomplete Event Data

no code implementations7 Nov 2018 Benjamin Mark, Garvesh Raskutti, Rebecca Willett

Multivariate Bernoulli autoregressive (BAR) processes model time series of events in which the likelihood of current events is determined by the times and locations of past events.

Time Series

Graph-based regularization for regression problems with alignment and highly-correlated designs

no code implementations20 Mar 2018 Yuan Li, Benjamin Mark, Garvesh Raskutti, Rebecca Willett, Hyebin Song, David Neiman

This work considers a high-dimensional regression setting in which a graph governs both correlations among the covariates and the similarity among regression coefficients -- meaning there is \emph{alignment} between the covariates and regression coefficients.

Model Selection

Network Estimation from Point Process Data

no code implementations13 Feb 2018 Benjamin Mark, Garvesh Raskutti, Rebecca Willett

Using our general framework, we provide a number of novel theoretical guarantees for high-dimensional self-exciting point processes that reflect the role played by the underlying network structure and long-term memory.

Point Processes

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