Research map / Probabilistic and scientific modeling / Variational and generative sampling
Operator Variational Inference
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace KL operator with Langevin-Stein operator while keeping a mean-field Gaussian family (ablation) | Logistic factor analysis posterior approximation · Binarized MNIST, missing-data reconstruction | Completed data log-likelihood: -59.3 to -75.3 | Hurt |
| Use a variational program (neural network transform of noise) with the Langevin-Stein operator instead of a mean-field Gaussian (ablation) | Langevin-Stein OPVI posterior approximation · Binarized MNIST, missing-data reconstruction | Completed data log-likelihood: -75.3 to -58.9 | Helped |
| Variational program with Langevin-Stein operator vs mean-field Gaussian with KL | Logistic factor analysis · Binarized MNIST, missing-data reconstruction | Completed data log-likelihood: -59.3 to -58.9 | Helped |
| Langevin-Stein operator vs KL on mixture of Gaussians with Gaussian family | Bimodal mixture posterior · 1D mixture of two Gaussians | qualitative fit (mode capture) | No clear effect |
| Variational program with Langevin-Stein operator instead of Gaussian family | Bimodal mixture posterior · 1D mixture of two Gaussians | qualitative fit (mode capture) | Helped |
| Langevin-Stein operator objective instead of Gaussian-family KL | Optimization difficulty · Mixture of Gaussians | optimization ease | Hurt |
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