Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Bayesian inference via sparse Hamiltonian flows
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add periodic marginal quasi-refreshment steps | Sparse Hamiltonian flows · Augmented posterior approximation | KL divergence | Helped |
| Use a uniformly subsampled data subset as the coreset basis and optimize its weights | Bayesian Gaussian location model · Bayesian Gaussian location model | KL divergence: 0 | Helped |
| Use coreset-based sparse leapfrog dynamics instead of full-data flow dynamics | Hamiltonian flow inference · Synthetic Gaussian | density evaluation and sample generation time | Helped |
| Use sparse Hamiltonian flows instead of competing Hamiltonian-based variational inference methods | Posterior approximation · Synthetic Gaussian | KL divergence | Helped |
| Use sparse Hamiltonian flows instead of competing methods | Posterior approximation · Bayesian linear regression | Gaussian-approximated marginal KL divergence | Helped |
| Use sparse Hamiltonian flows instead of uniform subsampling or Hilbert-OMP | Coreset construction · Bayesian linear regression | Gaussian-approximated marginal KL divergence | Helped |
| Use sparse Hamiltonian flows instead of competing methods | Posterior approximation · Bayesian logistic regression | posterior approximation quality | Helped |
| Use momentum tempering without quasi-refreshment (ablation) | Hamiltonian flow variational family · Gaussian target | KL divergence | Hurt |
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