Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Robust and Scalable Bayesian Online Changepoint Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DSM_m-BOCD instead of standard BOCD | BOCD · Synthetic data with outliers | PPV: 0.6±0.128 -> 0.907±0.154 | Helped |
| Use DSM_m-BOCD instead of standard BOCD | BOCD · Synthetic data with outliers | TPR: 0.833±0.149 -> 0.883±0.13 | Helped |
| Use DSM_m-BOCD instead of standard BOCD | BOCD · Synthetic data with outliers | Detection delay: 1.05±1.545 -> 1.643±0.475 | Hurt |
| Use the proposed robust diffusion matrix m rather than m=I_d (ablation) | DSM_m-BOCD · Contaminated synthetic data | False changepoint detections | Helped |
| Use DSM_m-BOCD instead of standard BOCD | BOCD · DJIA Twitter flash crash | False changepoint detections: 3 -> no CPs | Helped |
| Use DSM_m-BOCD instead of standard BOCD | BOCD · Well-log | False changepoint detections | Helped |
| Use DSM_m-BOCD instead of β-BOCD | BOCD · Well-log | Runtime: about 10 times less | Helped |
| Use DSM_m-BOCD instead of standard BOCD | BOCD · FTT and Bitcoin crash | Changepoint detection | No clear effect |
| Use DSM_m-BOCD | BOCD · Multivariate synthetic data | Changepoint detection | Helped |
Closest papers by meaning
- Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay (ICML 2020 · similarity 0.91)
- Optimal Online Change Detection via Random Fourier Features (NeurIPS 2025 · similarity 0.86)
- Conjugate Bayesian Two-step Change Point Detection for Hawkes Process (NeurIPS 2024 · similarity 0.85)
- Outlier-robust Kalman Filtering through Generalised Bayes (ICML 2024 · similarity 0.84)
- Reducing sequential change detection to sequential estimation (ICML 2024 · similarity 0.83)
- Triadic-OCD: Asynchronous Online Change Detection with Provable Robustness, Optimality, and Convergence (ICML 2024 · similarity 0.82)
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