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Tracking dynamic point processes on networks
Published on Oct 29, 20142242 Views
Cascading chains of events are a salient feature of many real-world social, biological, and financial networks. In social networks, social reciprocity accounts for retaliations in gang interactions, p
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Chapter list
Tracking In uences within Dynamic Networks00:00
Cascading chains of interactions00:52
Epidemology01:35
Functional neural network connectivity02:39
Seismology03:28
Finance04:17
Sequence of events04:24
Mirror descent06:40
Loss functions and point processes - 107:49
Loss functions and point processes - 209:08
Mirror descent in our setting10:34
Static regret bounds11:14
Tracking regret against time-varying reference models12:05
Multivariate Hawkes Processes - 113:15
Multivariate Hawkes Processes - 213:38
Multivariate Hawkes Processes - 314:02
Multivariate Hawkes Processes - 414:04
Multivariate Hawkes Processes - 514:10
Multivariate Hawkes Processes - 614:14
Multivariate Hawkes Processes - 714:15
Multivariate Hawkes Processes - 814:15
Multivariate Hawkes Processes - 914:16
Multivariate Hawkes Processes - 1014:17
Multivariate Hawkes Processes - 1114:18
Multivariate Hawkes Processes - 1214:19
Multivariate Hawkes Processes - 1314:20
Multivariate Hawkes Processes - 1414:20
Multivariate Hawkes Processes - 1514:30
Multivariate Hawkes Processes - 1714:30
Multivariate Hawkes Processes - 1615:16
Dynamics in Hawkes processes15:27
A dynamical model perspective of Hawkes processes16:30
Dynamic Mirror Descent (DMD)17:11
Contractivity18:51
Tracking W - 119:46
Tracking W - 221:27
Proposed method22:28
Main result23:20
Experimental results - 124:02
Experimental results - 225:09
Experimental results - 326:07
Conclusions27:10
Thank you27:45