Track of developments in Temporal Point Processes (TPPs)
- Lecture notes, tutorials and blogs
- Papers
- Survey and review
- Recurrent history encoders (RNNs)
- Set aggregation history encoders (Transformer)
- Continuous time state
- Intensity free and likelihood free
- Conditionally dependent modeling of time and mark
- Reinforcement learning
- Noise contrastive learning
- Long range event forecasting
- Neural ordinary differential equation (Neural ODE)
- Counterfactual modeling
- Normalizing flows and Efficient TPPs
- Intermittent TPPS
- TPPs and graphs
- Hawkes process
- Adversarial
- Retrieval
- Others
- Workshop papers
- Workshops
- Applications
- Books
- Other awesome-tpp repos
- Lecture notes
- Tutorial
- Graphical Models Meet Temporal Point Processes [UAI-2022]
- By Thinklab [KDD-2019]
- Learning with TPP [ICML-2018]
- Blog
- Exploring Generative Neural Temporal Point Process [TMLR-2022]
- An Empirical Study: Extensive Deep Temporal Point Process [arXiv-2021]
- Neural Temporal Point Processes: A Review [IJCAI-2021]
- Recent Advance in Temporal Point Process: from Machine Learning Perspective [2019]
- Fully Neural Network based Model for General Temporal Point Processes [NeurIPS-2019]
- Marked Temporal Dynamics Modeling based on Recurrent Neural Network [PAKDD-2017]
- Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks [AAAI-2017]
- RMTPP: Recurrent Marked Temporal Point Processes: Embedding Event History to Vector [KDD-2016]
- Transformer Embeddings of Irregularly Spaced Events and Their Participants [ICLR-2022]
- Deep Fourier Kernel for Self-Attentive Point Processes [AISTATS-2021]
- SAHP: Self-Attentive Hawkes Processes [ICML-2020]
- THP: Transformer Hawkes Process [ICML-2020]
- User-Dependent Neural Sequence Models for Continuous-Time Event Data [NeurIPS-2020]
- NHP: The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process [NeurIPS-2017]
- LNM: Intensity-Free Learning of Temporal Point Processes [ICLR-2020]
- Learning Conditional Generative Models for Temporal Point Processes [AAAI-2018]
- Wasserstein Learning of Deep Generative Point Process Models [NeurIPS-2017]
- Uncertainty on Asynchronous Time Event Prediction [NeurIPS-2019]
- Neural Temporal Point Processes For Modelling Electronic Health Records [ML4H at NeurIPS-2020]
- Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes [CIKM-2022]
- Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes [AAAI-2023] [
- Learning Temporal Point Processes via Reinforcement Learning [NeurIPS-2018]
- Deep Reinforcement Learning of Marked Temporal Point Processes [NeurIPS-2018]
- Noise-contrastive estimation for multivariate point processes [NeurIPS-2020]
- INITIATOR: Noise-contrastive Estimation for Marked Temporal Point Process [IJCAI-2018]
- Neural multi-event forecasting on spatio-temporal point processes using probabilistically enriched transformers [arXiv-2022]
- HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences [NeurIPS-2022]
- DualTPP: Long Horizon Forecasting With Temporal Point Processes [WSDM-2021]
- Neural Spatio-Temporal Point Processes [ICLR-2021]
- Learning Neural Event Functions for Ordinary Differential Equations [ICLR-2021]
- Neural Jump Stochastic Differential Equations [NeurIPS-2019]
- Latent ODEs for Irregularly-Sampled Time Series [NeurIPS-2019]
- Counterfactual Temporal Point Processes [NeurIPS-2022]
- Causal Inference for Event Pairs in Multivariate Point Processes [NeurIPS-2021]
- CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods [ICML-2020]
- Uncovering Causality from Multivariate Hawkes Integrated Cumulants [ICML-2017]
- Graphical Modeling for Multivariate Hawkes Processes with Nonparametric Link Functions [Journal of Time Series Analysis-2017]
- ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences [KDD-2022]
- TriTPP: Fast and Flexible Temporal Point Processes with Triangular Maps [NeurIPS-2020]
- FastPoint: Scalable Deep Point Processes [ECML KDD-2019]
- Point Process Flows [arXiv-2019]
- Learning Temporal Point Processes with Intermittent Observations [AISTATS-2021]
- Imputing Missing Events in Continuous-Time Event Streams [ICML-2019]
- TREND: TempoRal Event and Node Dynamics for Graph Representation Learning [WWW-2022]
- Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functions [Research track KDD-2021]
- Learning Neural Point Processes with Latent Graphs [WWW-2021]
- Modeling Event Propagation via Graph Biased Temporal Point Process [IEEE Transactions on Neural Networks and Learning Systems-2020]
- DyRep: Learning Representations over Dynamic Graphs [ICLR-2019]
- Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs [ICML-2017]
- CoEvolve: A Joint Point Process Model for Information Diffusion and Network Co-evolution [JMLR-2017]
-
Efficient Non-parametric Bayesian Hawkes Processes [IJCAI-2019]
-
Nonlinear Hawkes Processes in Time-Varying System [arXiv-2021]
-
ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences [KDD-2022]
- Improving Maximum Likelihood Estimation of Temporal Point Process via Discriminative and Adversarial Learning [IJCAI-2018]
- Adversarial Training Model Unifying Feature Driven and Point Process Perspectives for Event Popularity Prediction [CIKM-2018]
- NEUROSEQRET: Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences [AAAI-2022]
- Neural Point Process for Learning Spatiotemporal Event Dynamics [L4DC-2022]
- Deep Structural Point Process for Learning Temporal Interaction Networks [ECML PKDD-2021]
- Learning to Select Exogenous Events for Marked Temporal Point Process [NeurIPS-2021]
- NEST: Imitation Learning of Neural Spatio-Temporal Point Processes [arXiv-2021]
- UNIPoint: Universally Approximating Point Processes Intensities [AAAI-2021]
- NDTT: Neural Datalog through time: Informed temporal modeling via logical specification [ICML-2020]
- Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information [KDD-2019]
- Semi-supervised Learning for Marked Temporal Point Processes [IJCAI Workshop-2021]
- Intermittent Demand Forecasting with Deep Renewal Processes [Workshop on TPPs at NeurIPS-2019]
- Learning with TPPs [NeurIPS-2019]
- Recommendation
- Human mobility and activity
- Event clustering
- Anomaly detection
- Detecting Anomalous Event Sequences with Temporal Point Processes [NeurIPS-2021]
- Detecting Changes in Dynamic Events Over Networks [IEEE Transactions on Signal and Information Processing over Networks-2017]
- Detecting Anomalous Event Sequences with Temporal Point Processes [NeurIPS-2021]
- Optimal control
- Misinformation on social media
- Recurrent Poisson process unit for speech recognition [AAAI-2019]
- Point process latent variable models of larval zebrafish behavior [NeurIPS-2018]
- An introduction to the theory of point processes: Volume I: Elementary theory and methods: Daley, D.J., Vere-Jones, D
- An introduction to the theory of point processes: Volume II: General theory and structure: Daley, D.J., Vere-Jones, D