Research map / Time series / Causal and generative time series
Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace additive Hawkes triggering (HP-GS) with CGPRPP | point process · Synthetic 1 (renewal) | test log-likelihood: -2671 -> -2455 | Helped |
| CGPRPP instead of HP-GS on Hawkes-generated data | point process · Synthetic 2 (Hawkes) | test log-likelihood: -4074 -> -4071 | No clear effect |
| CGPRPP instead of HP-GS on bursty IPTV | point process · IPTV | test log-likelihood: -1.477e+05 -> -1.479e+05 | No clear effect |
| CGPRPP instead of HP-LS (discretized least squares) | point process · IPTV | test log-likelihood: -1.779e+05 -> -1.479e+05 | Helped |
| CGPRPP instead of HP-GS on MIMIC lab orders | point process · MIMIC class 355 | test log-likelihood: -3668 -> -3249 | Helped |
| CGPRPP instead of best baseline on MIMIC classes 550 and 18 | point process · MIMIC class 550 | test log-likelihood: -1039 -> -1175 | Hurt |
| increase number of regression terms Q (1 to 40) (ablation) | CGPRPP · Synthetic 2 (Hawkes) | influence recovery | Helped |
Closest papers by meaning
- Learning Hawkes Processes from Short Doubly-Censored Event Sequences (ICML 2017 · similarity 0.89)
- Self-Adaptable Point Processes with Nonparametric Time Decays (NeurIPS 2021 · similarity 0.89)
- EasyTPP: Towards Open Benchmarking Temporal Point Processes (ICLR 2024 · similarity 0.87)
- Deep Continuous-Time State-Space Models for Marked Event Sequences (NeurIPS 2025 · similarity 0.87)
- Interacting Diffusion Processes for Event Sequence Forecasting (ICML 2024 · similarity 0.86)
- Hyper Hawkes Processes: Interpretable Models of Marked Temporal Point Processes (NeurIPS 2026 · similarity 0.86)
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