Learning Triggering Kernels for Multi-dimensional Hawkes Processes

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Scalable Influence Estimation in Continuous-Time Diffusion Networks (2013, background): “A sequence of recent work has argued that modeling cascade data and information diffusion using continuous-time diffusion networks can provide significantly more accurate models than discretetime models [8, 9, 10, 11, 12, 13, 14, 15].”
Quantifying herding effects in crowd wisdom (2014, background): “(6) must satisfy the Euler-Lagrange equation [27] (proof referred to Appendix): 2A(t)f(t) +B(t)− λf (t) = 0 (7) where g′′(·) is the second order derivate of g(·).”
Hawkes Processes in Finance (2015, background): “the subject of numerous academical papers (see for instance [70]).”
HAWKES PROCESSES ON LARGE NETWORKS (2016, unknown): “[40]) and epidemiology like for instance viral diffusion on a network (Hang-Zha [39]), to name but a few.”
Time-Sensitive Recommendation From Recurrent User Activities (2015, methodology): “Furthermore, we can even learn the triggering kernel directly using nonparametric methods [8, 30].”
Neural Jump Stochastic Differential Equations (2019, unknown): “The self-exciting nature of the Hawkes process has made it a key model within machine learning and information science [22, 23, 24, 25, 26, 27, 28, 29].”