Research map / Probabilistic and scientific modeling / Variational and generative sampling
Gradient-based Adaptive Markov Chain Monte Carlo
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| adapt a full-covariance MALA proposal using the fast gradient approximation | MALA · Neal's Gaussian | Min ESS/s: 51.28 (1.64) -> 161.70 (15.07) | Helped |
| adapt a full-covariance MALA proposal using the fast gradient approximation | MALA · Caravan | Min ESS/s: 5.29 (0.38) -> 9.94 (2.64) | Helped |
| use exact Hessian gradients instead of stopping the gradient through the proposal-state gradient (ablation) | gadMALAf · Neal's Gaussian | Min ESS/s: 161.70 (15.07) -> 92.34 (7.11) | Hurt |
| use exact Hessian gradients instead of stopping the gradient through the proposal-state gradient (ablation) | gadMALAf · Caravan | Min ESS/s: 9.94 (2.64) -> 0.70 (0.16) | Hurt |
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