Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Multivariate Hawkes Processes for Large-Scale Inference
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LRHP instead of MEMIP | Next-event prediction on MemeTracker · MemeTracker MT1–MT3 | AUC (%): 86.4 vs 85.8; 90.1 vs 91.7; 84.3 vs 85.9 | Mixed |
| Use LRHP instead of MEMIP | Next-event prediction on MemeTracker · MemeTracker MT1–MT3 | Accuracy (%): 98.8 vs 99.2; 91.7 vs 93.7; 88.6 vs 91.1 | Mixed |
| Use LRHP instead of MEMIP | MemeTracker inference · MemeTracker MT2–MT3 | training time (secs): 281 vs 7.14 \op{\cdot} 10^3; 1.95 \op{\cdot} 10^3 vs 1.74 \op{\cdot} 10^5 | Helped |
| Use LRHP instead of NAIVE frequency ranking | Next-event prediction · MemeTracker MT4 | AUC (%): 92.6 vs 88.2 | Helped |
| Use LRHP instead of NAIVE frequency ranking | Next-event prediction · MemeTracker MT4 | Accuracy (%): 94.8 vs 87.5 | Helped |
| Use LRHP instead of MEMIP | MemeTracker inference · MemeTracker MT1 | training time (secs): 8.34 vs 3.16 | Hurt |
| Approximate triggering kernels with the proposed model on simulated Hawkes data | LRHP · Synthetic data (8 datasets) | normalized L2 error: 4.2% average; 3.8% minimum; 4.7% maximum | Helped |
| Vary the low-rank approximation rank r (ablation) | LRHP · MemeTracker MT3 | Accuracy | No clear effect |
Closest papers by meaning
- Learning Hawkes Processes from a handful of events (NeurIPS 2019 · similarity 0.89)
- Online Learning for Multivariate Hawkes Processes (NeurIPS 2017 · similarity 0.88)
- Uncertainty Quantification for Inferring Hawkes Networks (NeurIPS 2020 · similarity 0.88)
- A Representer Theorem for Hawkes Processes via Penalized Least Squares Minimization (ICLR 2026 · similarity 0.88)
- Variational Inference for Sparse Gaussian Process Modulated Hawkes Process (AAAI 2020 · similarity 0.88)
- Hyper Hawkes Processes: Interpretable Models of Marked Temporal Point Processes (NeurIPS 2026 · similarity 0.85)
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