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| { | |
| "schema_version": 1, | |
| "title": "Reproduction: Predictive Variational Inference (PVI)", | |
| "emoji": "🎯", | |
| "space_id": "snaykey/repro-predictive-vi", | |
| "paper": { | |
| "openreview_id": "dVIts0fNb4" | |
| }, | |
| "tags": [ | |
| "icml2026-repro", | |
| "paper-dVIts0fNb4" | |
| ], | |
| "updated_at": "2026-08-02T00:00:00+00:00", | |
| "root": { | |
| "slug": "index", | |
| "title": "Reproduction: Predictive Variational Inference (PVI)", | |
| "file": "pages/index.md", | |
| "children": [ | |
| { | |
| "slug": "claim-1-framework", | |
| "title": "Predictive variational inference (PVI) replaces the KL-divergence objective of standard VI with optimization of the posterior predictive distribution against the true data-generating process, using proper scoring rules (Section: Main Framework).", | |
| "file": "pages/claim-1-framework/page.md", | |
| "children": [] | |
| }, | |
| { | |
| "slug": "claim-2-prop1-consistency", | |
| "title": "Proposition 1 establishes that PVI solutions converge to the predictively optimal parameter as sample size grows, even under model misspecification (Proposition 1).", | |
| "file": "pages/claim-2-prop1-consistency/page.md", | |
| "children": [] | |
| }, | |
| { | |
| "slug": "claim-3-corollaries", | |
| "title": "Corollary 1 shows PVI recovers the true parameter when the model is correctly specified, and Corollary 2 shows it recovers the population parameter distribution when misspecified with population-varying parameters (Corollary 1, Corollary 2).", | |
| "file": "pages/claim-3-corollaries/page.md", | |
| "children": [] | |
| }, | |
| { | |
| "slug": "claim-4-scoring-rules", | |
| "title": "PVI is implemented with three proper scoring rules -- logarithmic, quadratic, and continuous ranked probability score (CRPS) -- with the CRPS variant enabling likelihood-free inference for intractable simulators (Section on scoring rules).", | |
| "file": "pages/claim-4-scoring-rules/page.md", | |
| "children": [] | |
| }, | |
| { | |
| "slug": "claim-5-cryoem-heterogeneity", | |
| "title": "In a cryo-EM protein conformation inference experiment, PVI recovers the true population distribution of parameters while standard Bayesian VI collapses to a point estimate (Section: CryoEM Protein Inference experiment).", | |
| "file": "pages/claim-5-cryoem-heterogeneity/page.md", | |
| "children": [] | |
| }, | |
| { | |
| "slug": "claim-6-posteriordb", | |
| "title": "Across 7 models from PosteriorDB, PVI improves held-out predictive performance compared to standard variational inference (Section: Benchmark PosteriorDB experiment).", | |
| "file": "pages/claim-6-posteriordb/page.md", | |
| "children": [] | |
| }, | |
| { | |
| "slug": "executive-summary", | |
| "title": "executive-summary", | |
| "file": "pages/executive-summary/page.md", | |
| "children": [] | |
| }, | |
| { | |
| "slug": "conclusion", | |
| "title": "conclusion", | |
| "file": "pages/conclusion/page.md", | |
| "children": [] | |
| } | |
| ] | |
| } | |
| } | |