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Point process models are of great importance in real world applications.
Spectra of some self-exciting and mutually exciting point processes
Hawkes, A. G · 1971
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Statistical models for earthquake occurrences and residual analysis for point processes
Ogata, Y · 1988
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The integer-valued autoregressive (inar (p)) model
Jin-Guan, D · 1991
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Strong mixing condition for hawkes processes and application to whittle estimation from count data
Cheysson, F · 2003
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Boosting and differential privacy
Dwork, C · 2010
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Self-exciting point process modeling of crime
Mohler, G. O · 2011
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Tensor kernel recovery for spatio-temporal hawkes processes
Sheen, H · 2011
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Like like alike: joint friendship and interest propagation in social networks
Yang, S · 2011
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Revisiting frank-wolfe: Projection-free sparse convex optimization
Jaggi, M · 2013
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Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
Shamir, O · 2013
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Stochastic gradient descent with differentially private updates
Song, S · 2013
Cited alongside, same era.
Mixture of mutually exciting processes for viral diffusion
Yang, S · 2013
Cited alongside, same era.
Learning social infectivity in sparse low-rank networks using multi-dimensional hawkes processes
Zhou, K · 2013
Cited alongside, same era.
Private empirical risk minimization, revisited
Bassily, R · 2014
Cited alongside, same era.
The algorithmic foundations of differential privacy
Dwork, C · 2014
Cited alongside, same era.
Lasso and probabilistic inequalities for multivariate point processes
Hansen, N. R · 2015
Cited alongside, same era.
Hawkes and inar processes
Kirchner, M · 2016
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Bacry, E · 2017
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An estimation procedure for the hawkes process
Kirchner, M · 2017
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The neural hawkes process: A neurally self-modulating multivariate point process
Mei, H · 2017
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A nonparametric estimation procedure for the hawkes process: comparison with maximum likelihood estimation
Kirchner, M · 2018
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Privacy preserving, crowd sourced crime hawkes processes
Mohler, G · 2018
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Talwar, K · 2015
Cited alongside, same era.
Estimation in high dimensions: a geometric perspective
Vershynin, R · 2015
Cited alongside, same era.
Deep learning with differential privacy
Abadi, M · 2016
Cited alongside, same era.
Recurrent marked temporal point processes: Embedding event history to vector
Du, N · 2016
Cited alongside, same era.
Sparse and low-rank multivariate hawkes processes
Bacry, E
Cited in the paper.
Hawkes processes in finance
Bacry, E
Cited in the paper.
Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation
Wang, L · 2018
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Mixing conditions for multivariate hawkes processes
Boly, O · 2020
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Privacy for spatial point process data
Walder, A · 2020
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Zuo, S · 2020
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