Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use QNC instead of UNIF, GIGA, or IHT | Bayesian coreset construction · Synthetic Gaussian location model | reverse KL divergence | Helped |
| Use QNC instead of UNIF, GIGA, or IHT | Bayesian coreset construction · Flight delays sparse linear regression | reverse KL divergence | Helped |
| Use QNC instead of LAP | Bayesian coreset construction · Flight delays sparse linear regression, coreset sizes above 500 | reverse KL divergence | Helped |
| Use QNC instead of UNIF, GIGA, or IHT | Bayesian coreset construction · Heavy-tailed Bayesian logistic regression | reverse KL divergence | Helped |
| Use QNC instead of LAP | Bayesian coreset construction · Heavy-tailed Bayesian logistic regression, coreset sizes above 500 | reverse KL divergence | Helped |
| Use QNC instead of LAP | Bayesian coreset construction · Bayesian radial basis function regression | reverse KL divergence | Helped |
| Use QNC instead of UNIF, GIGA, or IHT | Bayesian coreset construction · Bayesian radial basis function regression, coreset sizes of 1000 and above | reverse KL divergence | Helped |
| Use LAP instead of QNC | Bayesian coreset construction · Synthetic Gaussian location model | reverse KL divergence | No clear effect |
| Estimate H using an additional uniform subsample rather than all data (ablation) | QNC · Bayesian coreset construction | coreset quality | Hurt |
| Vary S, K_tune, and tau within reasonable ranges (ablation) | QNC · Experiments reported in the paper | coreset quality | No clear effect |
Closest papers by meaning
- Coresets for Scalable Bayesian Logistic Regression (NeurIPS 2016 · similarity 0.92)
- Sparse Variational Inference: Bayesian Coresets from Scratch (NeurIPS 2019 · similarity 0.92)
- General bounds on the quality of Bayesian coresets (NeurIPS 2024 · similarity 0.91)
- Stable Coresets via Posterior Sampling: Aligning Induced and Full Loss Landscapes (NeurIPS 2025 · similarity 0.86)
- Variational Bayesian Pseudo-Coreset (ICLR 2025 · similarity 0.85)
- Scalable Bayesian Learning with posteriors (ICLR 2025 · similarity 0.85)
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