no code implementations • 20 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.
no code implementations • 13 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.
no code implementations • 7 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.
no code implementations • 16 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.