% Key papers for Generative temporal point processes (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2019_event_14158,
  title = {{Event Cameras, Contrast Maximization and Reward Functions: An Analysis}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{aaai2018_learning_106051,
  title = {{Learning Conditional Generative Models for Temporal Point Processes}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/12072},
}

@inproceedings{neurips2019_sequential_58979,
  title = {{Sequential Neural Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14585},
}

@inproceedings{neurips2019_uncertainty_59130,
  title = {{Uncertainty on Asynchronous Time Event Prediction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14614},
}

@inproceedings{iclr2020_intensity_29550,
  title = {{Intensity-Free Learning of Temporal Point Processes}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=HygOjhEYDH},
}

@inproceedings{aaai2022_deepgpd_1948,
  title = {{DeepGPD: A Deep Learning Approach for Modeling Geospatio-Temporal Extreme Events}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/04245-deepgpd-a-deep-learning-approach-for-modeling-geospatio-temporal-extreme-events/},
}

@inproceedings{neurips2017_predicting_56589,
  title = {{Predicting User Activity Level In Point Processes With Mass Transport Equation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8955},
}

@inproceedings{neurips2019_nonparametric_58691,
  title = {{Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13332},
}

@inproceedings{aaai2021_unipoint_1572,
  title = {{UNIPoint: Universally Approximating Point Processes Intensities}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/09685-unipoint-universally-approximating-point-processes-intensities/},
}

@inproceedings{icml2017_variational_36451,
  title = {{Variational Policy for Guiding Point Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/798},
}

@inproceedings{aaai2018_proper_102348,
  title = {{Proper Loss Functions for Nonlinear Hawkes Processes}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11615},
}

@inproceedings{aaai2022_learning_2333,
  title = {{Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/04005-learning-temporal-point-processes-for-efficient-retrieval-of-continuous-time-event-sequences/},
}

@inproceedings{cvpr2025_ebs_26051,
  title = {{EBS-EKF: Accurate and High Frequency Event-based Star Tracking}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33933},
}

@inproceedings{iclr2024_easytpp_34363,
  title = {{EasyTPP: Towards Open Benchmarking Temporal Point Processes}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18712},
}

@inproceedings{aaai2023_c_3204,
  title = {{C-NTPP: Learning Cluster-Aware Neural Temporal Point Process}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25897},
}

@inproceedings{iclr2021_neural_30396,
  title = {{Neural Spatio-Temporal Point Processes}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2747},
}

@inproceedings{aaai2017_read_99787,
  title = {{Read the Silence: Well-Timed Recommendation via Admixture Marked Point Processes}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10496},
}

@inproceedings{aaai2025_kernel_9638,
  title = {{Kernel Representation Learning for Time Sequence: Algorithm, Theory, and Applications}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35232},
}

@inproceedings{icml2018_learning_36799,
  title = {{Learning Registered Point Processes from Idiosyncratic Observations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2045},
}

@inproceedings{neurips2021_detecting_62015,
  title = {{Detecting Anomalous Event Sequences with Temporal Point Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27787},
}

@inproceedings{neurips2024_decomposable_71953,
  title = {{Decomposable Transformer Point Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95355},
}

@inproceedings{neurips2025_deep_75341,
  title = {{Deep Continuous-Time State-Space Models for Marked Event Sequences}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=74SvE2GZwW},
}

@inproceedings{icml2024_interacting_43767,
  title = {{Interacting Diffusion Processes for Event Sequence Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34685},
}

@inproceedings{neurips2025_addressing_79094,
  title = {{Addressing Mark Imbalance in Integration-free Marked Temporal Point Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=lUtNvMiW3C},
}

@inproceedings{iclr2025_unlocking_80765,
  title = {{Unlocking Point Processes through Point Set Diffusion}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/31003},
}

@inproceedings{iclr2026_in_87928,
  title = {{In-Context Learning of Temporal Point Processes with Foundation Inference Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=h9HwUAODFP},
}

@inproceedings{neurips2026_hyper_90306,
  title = {{Hyper Hawkes Processes: Interpretable Models of Marked Temporal Point Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/149728},
}

@inproceedings{neurips2026_seahorse_91509,
  title = {{Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/139192},
}

@inproceedings{aaai2026_metp_107096,
  title = {{METP: Multi-Granularity Integration of External Covariates for Temporal Point Processes}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39448},
}

@inproceedings{aaai2026_st_107498,
  title = {{ST-TPP: Learning Semi-Transductive Temporal Point Processes with Gromov-Wasserstein Barycentric Regularization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39850},
}

@inproceedings{aaai2026_itpp_107786,
  title = {{ITPP: Learning Disentangled Event Dynamics in Marked Temporal Point Processes}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40138},
}

@inproceedings{icml2024_beyond_43791,
  title = {{Beyond Point Prediction: Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34661},
}

@inproceedings{icml2024_neural_44879,
  title = {{Neural Jump-Diffusion Temporal Point Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33573},
}

@inproceedings{icml2025_residual_46393,
  title = {{Residual TPP: A Unified Lightweight Approach for Event Stream Data Analysis}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46171},
}

@inproceedings{icml2026_forward_50080,
  title = {{Forward-Chaining Temporal Point Process}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=8tjGapjQ5J},
}

@inproceedings{icml2026_branching_54397,
  title = {{Branching Diffusion for Point Processes in Time and Space}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=oWyvNaX6op},
}

@inproceedings{iclr2026_ten_85192,
  title = {{TEN-DM: Topology-Enhanced Diffusion Model for Spatio-Temporal Event Prediction}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=BZ1vutP53o},
}