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Chaos & Complex Systems Seminar
Inference for high-dimensional self-exciting point processes
Date: Tuesday, November 14th
Time: 12:05 pm - 1:00 pm
Place: 4274 Chamberlin (refreshments will be served)
Speaker: Becca Willett, UW Department of Electrical and Computer Engineering
Abstract: In a variety of settings, our only glimpse at a network’s structure is through observations of a corresponding dynamical system. For instance, in a social network, we may observe a time series of members’ activities, such as posts on social media. In biological neural networks, firing neurons can trigger or inhibit the firing of their neighbors, so that information about the network structure is embedded within spike train observations. These processes are “self-exciting” in that the likelihood of future events depends on past events. In these and other settings, a network’s structure corresponds to the extent to which one node’s activity stimulates or inhibits activity in another node. In this talk, I will describe sparsity-regularized inference methods and theoretical guarantees that reflect the role of the network’s degree distribution and other network properties in determining the complexity of the inference problem for large-scale networks. In addition, we will see how these techniques can be used in applications ranging from criminology to predicting adverse drug reactions.
Host: Clint Sprott
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