Research map / Probabilistic and scientific modeling / Variational and generative sampling
Yes, but Did It Work?: Evaluating Variational Inference
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Tighten the relative ELBO stopping threshold from 10^{-2} to 10^{-5} (ablation) | ADVI · Bayesian linear regression | PSIS k-hat: 4.4 -> 0.61 | Helped |
| Use non-centered instead of centered parameterization (ablation) | ADVI · Eight-Schools Model | joint PSIS k-hat: 1.00 -> 0.64 | Helped |
| Use non-centered instead of centered parameterization (ablation) | ADVI · Eight-Schools Model | bias in first and second moment estimates | Helped |
| Apply PSIS reweighting to the VI estimate | ADVI · Bayesian linear regression | estimation error | Helped |
| Apply PSIS reweighting instead of vanilla importance sampling | ADVI · Logistic regression with varying design correlation | RMSE of first and second moments | Helped |
| Increase design-matrix correlation | Mean-field Gaussian ADVI · Logistic regression | PSIS k-hat | Hurt |
| Use mean-field Gaussian ADVI for the horseshoe posterior | ADVI · Leukemia microarray cancer classification | PSIS k-hat: 9.8 | Hurt |
Closest papers by meaning
- Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation (NeurIPS 2019 · similarity 0.86)
- Forward $\chi^2$ Divergence Based Variational Importance Sampling (ICLR 2024 · similarity 0.86)
- Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians (ICML 2024 · similarity 0.85)
- Generalized Variational Inference via Optimal Transport (AAAI 2024 · similarity 0.85)
- Variational Inference with Mixtures of Isotropic Gaussians (NeurIPS 2025 · similarity 0.84)
- Least squares variational inference (NeurIPS 2025 · similarity 0.84)
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