mlsanityData to 14 Oct 2026

Wasserstein Learning of Deep Generative Point Process Models

arXiv 1705.08051 ↗First posted 23 May 2017cs.LG stat.MLcited by 46

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Abstract (arXiv)

Point processes are becoming very popular in modeling asynchronous sequential data due to their sound mathematical foundation and strength in modeling a variety of real-world phenomena. Currently, they are often characterized via intensity function which limits model's expressiveness due to unrealistic assumptions on its parametric form used in practice. Furthermore, they are learned via maximum likelihood approach which is prone to failure in multi-modal distributions of sequences. In this paper, we propose an intensity-free approach for point processes modeling that transforms nuisance processes to a target one. Furthermore, we train the model using a likelihood-free leveraging Wasserstein distance between point processes. Experiments on various synthetic and real-world data substantiate the superiority of the proposed point process model over conventional ones.

Key facts. Cited by 46 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 May 2017.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 8; 2022: 7; 2023: 12; 2024: 11; 2025: 6; 2026: 1.

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  1. 2021Neural Temporal Point Processes: A Reviewcited by 114
  2. 2023Spatio-temporal Diffusion Point Processescited by 52
  3. 2021Neural Point Process for Learning Spatiotemporal Event Dynamicscited by 42
  4. 2022Exploring Generative Neural Temporal Point Processcited by 30
  5. 2023Add and Thin: Diffusion for Temporal Point Processescited by 23
  6. 2021Long Horizon Forecasting With Temporal Point Processescited by 20
  7. 2022Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequencescited by 14
  8. 2023On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Datacited by 14
  9. 2024Unlocking Point Processes through Point Set Diffusioncited by 14
  10. 2023Automatic Integration for Spatiotemporal Neural Point Processescited by 13
  11. 2021Region Invariant Normalizing Flows for Mobility Transfercited by 12
  12. 2022ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequencescited by 11
34 more
  1. 2021Neural Spectral Marked Point Processescited by 10
  2. 2025In-Context Learning of Temporal Point Processes with Foundation Inference Modelscited by 10
  3. 2022GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacycited by 9
  4. 2024EventFlow: Forecasting Temporal Point Processes with Flow Matchingcited by 8
  5. 2025Edit-Based Flow Matching for Temporal Point Processescited by 8
  6. 2022Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processescited by 7
  7. 2023Non-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Predictioncited by 7
  8. 2024Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processescited by 7
  9. 2023Modeling Events and Interactions through Temporal Processes -- A Surveycited by 6
  10. 2023A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphscited by 5
  11. 2024Mamba Hawkes Processcited by 5
  12. 2025Neural Spatiotemporal Point Processes: Trends and Challengescited by 5
  13. 2022Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processescited by 3
  14. 2024RoTHP: Rotary Position Embedding-based Transformer Hawkes Processcited by 3
  15. 2025Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approachescited by 3
  16. 2021Scalable Marked Point Processes for Exchangeable and Non-Exchangeable Event Sequencescited by 2
  17. 2023Proactive Resource Request for Disaster Response: A Deep Learning-based Optimization Modelcited by 2
  18. 2024EarthquakeNPP: A Benchmark for Earthquake Forecasting with Neural Point Processescited by 2
  19. 2024Recurrent Neural Goodness-of-Fit Test for Time Seriescited by 2
  20. 2021Nonlinear Hawkes Process with Gaussian Process Self Effectscited by 1
  21. 2023Retrieving Continuous Time Event Sequences using Neural Temporal Point Processes with Learnable Hashingcited by 1
  22. 2023Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flowscited by 1
  23. 2023INTAGS: Interactive Agent-Guided Simulationcited by 1
  24. 2024Cumulative Distribution Function based General Temporal Point Processescited by 1
  25. 2024Non-Parametric Estimation of Multi-dimensional Marked Hawkes Processescited by 1
  26. 2024Adaptive Residual Transformation for Enhanced Feature-Based OOD Detection in SAR Imagerycited by 1
  27. 2025Likelihood-Free Estimation for Spatiotemporal Hawkes processes with missing data and application to predictive policingcited by 1
  28. 2019Imitation Learning of Neural Spatio-Temporal Point Processescited by 0
  29. 2021Hawkes Processes on Graphonscited by 0
  30. 2022CEP3: Community Event Prediction with Neural Point Process on Graphcited by 0
  31. 2023RigLSTM: Recurrent Independent Grid LSTM for Generalizable Sequence Learningcited by 0
  32. 2024Marked Temporal Bayesian Flow Point Processescited by 0
  33. 2025Differentiable Adversarial Attacks for Marked Temporal Point Processescited by 0
  34. 2026From Jumps to Signatures: a Generative Method for Temporal Point Processescited by 0