[1] J. M. L. Alcaraz and N. Strodthoff. Diffusion-based time series imputation and forecasting with structured state space models. arXiv preprint arXiv:2208.09399, 2022.
[2] J. Austin, D. D. Johnson, J. Ho, D. Tarlow, and R. Van Den Berg. Structured denoising diffusion models in discrete state-spaces. Advances in Neural Information Processing Systems, 34: 17981-17993, 2021.
[3] M. Biloš, K. Rasul, A. Schneider, Y. Nevmyvaka, and S. Gunnemann. Modeling temporal data as continuous functions with stochastic process diffusion. In International Conference on Machine Learning (ICML), 2023.
[4] T. Bosser and S. B. Taieb. On the predictive accuracy of neural temporal point process models for continuous-time event data. Transactions on Machine Learning Research, 2023.
[5] R. T. Chen, B. Amos, and M. Nickel. Neural spatio-temporal point processes. arXiv preprint arXiv:2011.04583, 2020.
[6] T. Chen and M. Zhou. Learning to jump: Thinning and thickening latent counts for generative modeling. arXiv preprint arXiv:2305.18375, 2023.
[7] D. R. Cox. Some statistical methods connected with series of events. Journal of the Royal Statistical Society: Series B (Methodological), 17(2):129-157, 1955.
[8] D. Daley and D. Vere-Jones. An Introduction to the Theory of Point Processes: Volume I: Elementary Theory and Methods. Probability and Its Applications. Springer New York, 2006.
[9] D. J. Daley and D. Vere-Jones. An introduction to the theory of point processes: volume II: general theory and structure. Springer Science & Business Media, 2007.
[10] K. Dascher-Cousineau, O. Shchur, E. E. Brodsky, and S. Gunnemann. Using deep learning for flexible and scalable earthquake forecasting. Geophysical Research Letters, 50(17), 2023.
[11] C. DR and I. COLL. The statistical analysis of dependencies in point processes. Stochastic Point Processes. Wiley: New York, pages 55-66, 1972.
[12] N. Du, H. Dai, R. Trivedi, U. Upadhyay, M. Gomez-Rodriguez, and L. Song. Recurrent marked temporal point processes: Embedding event history to vector. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1555-1564, 2016.
[13] J. Enguehard, D. Busbridge, A. Bozson, C. Woodcock, and N. Hammerla. Neural temporal point processes for modelling electronic health records. In Machine Learning for Health, pages 85–113. PMLR, 2020.
[14] A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola. A kernel two-sample test. The Journal of Machine Learning Research, 13(1):723-773, 2012.
[15] X. Han, H. Zheng, and M. Zhou. Card: Classification and regression diffusion models. arXiv preprint arXiv:2206.07275, 2022.
[16] A. Hasan, Y. Chen, Y. Ng, M. Abdelghani, A. Schneider, and V. Tarokh. Inference and sampling of point processes from diffusion excursions. In The 39th Conference on Uncertainty in Artificial Intelligence, 2023.
[17] A. G. Hawkes. Spectra of some self-exciting and mutually exciting point processes. Biometrika, 58(1):83-90, 1971.
[18] J. Ho, A. Jain, and P. Abbeel. Denoising diffusion probabilistic models. Neural Information Processing Systems (NeurIPS), 2020.
[19] C. Hong and C. Shelton. Deep neyman-scott processes. In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, volume 151, pages 3627-3646. PMLR, 2022.