Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Variational Inference for Sparse Gaussian Process Modulated Hawkes Process
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Select GP hyper-parameters with TELBO rather than CELBO (ablation) | VBHP · Sin | HLL: 3.488_{\pm0.400} -> 3.497_{\pm0.406} | Helped |
| Select GP hyper-parameters with TELBO rather than CELBO (ablation) | VBHP · Cos | HLL: 3.876_{\pm0.541} -> 3.878_{\pm0.548} | Helped |
| Select GP hyper-parameters with TELBO rather than CELBO (ablation) | VBHP · Exp | HLL: 2.826_{\pm0.491} -> 2.829_{\pm0.487} | Helped |
| Select GP hyper-parameters with TELBO rather than CELBO (ablation) | VBHP · ACTIVE | HLL: 1.824_{\pm1.159} -> 1.867_{\pm1.181} | Helped |
| Select GP hyper-parameters with TELBO rather than CELBO (ablation) | VBHP · SEISMIC | HLL: 3.143_{\pm0.895} -> 3.164_{\pm0.843} | Helped |
| Select GP hyper-parameters with TELBO rather than CELBO (ablation) | VBHP · Synthetic data (Sin, Cos, Exp) | L2 distance (triggering kernel) | No clear effect |
Closest papers by meaning
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- Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models (NeurIPS 2026 · similarity 0.87)
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