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"schema_version": 1,
"title": "Repro: Markov Chain Monte Carlo without Evaluating the Target",
"emoji": "chart",
"space_id": "snaykey/repro-speedbench",
"paper": {
"openreview_id": "dDkl5ZcyTl"
},
"tags": [
"icml2026-repro",
"paper-dDkl5ZcyTl"
],
"updated_at": "2026-07-26T00:00:00+00:00",
"root": {
"slug": "index",
"title": "Repro: Markov Chain Monte Carlo without Evaluating the Target",
"file": "pages/index.md",
"children": [
{
"slug": "executive-summary",
"title": "Executive summary",
"file": "pages/executive-summary/page.md",
"children": []
},
{
"slug": "claim-1-unification-common-procedure",
"title": "Section 2 unifies the exchange algorithm (Algorithm 1), PoissonMH (Algorithm 2), and TunaMH (Algorithm 3) as instances of one common auxiliary-variable procedure (Section 2).",
"file": "pages/claim-1-unification-common-procedure/page.md",
"children": []
},
{
"slug": "claim-2-meta-algorithm-two-aux-vars",
"title": "The proposed meta-algorithm (Algorithm 4) introduces two auxiliary variables, ω1 guiding the proposal and ω2 enabling target-ratio estimation, and Proposition 2 proves it preserves the target distribution Π as stationary (Algorithm 4, Proposition 2).",
"file": "pages/claim-2-meta-algorithm-two-aux-vars/page.md",
"children": []
},
{
"slug": "claim-3-proposition-1-reversibility",
"title": "Proposition 1 gives a reversibility condition on the auxiliary-variable estimator, R_{θ→θ'}(ω)π(θ|x)P_{θ→θ'}(ω) = π(θ'|x)P_{θ'→θ}(ω), that guarantees the resulting kernel targets the correct posterior (Proposition 1).",
"file": "pages/claim-3-proposition-1-reversibility/page.md",
"children": []
},
{
"slug": "claim-4-two-new-algorithms",
"title": "The framework yields two new algorithms: Locally Balanced PoissonMH (Algorithm 5), which uses gradient-informed locally balanced proposals with balancing functions such as Barker's g(t)=t/(1+t) or the MALA-type g(t)=sqrt(t), and TunaMH with an SGLD proposal (Algorithm 6), which uses independent minibatches for gradient estimation and acceptance-ratio evaluation (Algorithm 5, Algorithm 6).",
"file": "pages/claim-4-two-new-algorithms/page.md",
"children": []
},
{
"slug": "claim-5-doubly-intractable-and-tall-data",
"title": "The framework is designed to apply to both doubly-intractable distributions, where the likelihood f_θ(x)/Z(θ) has an intractable normalizing constant, and tall-data settings with millions to billions of observations, without needing to evaluate the full target density (Section 1).",
"file": "pages/claim-5-doubly-intractable-and-tall-data/page.md",
"children": []
},
{
"slug": "conclusion",
"title": "Conclusion",
"file": "pages/conclusion/page.md",
"children": []
}
]
}
} |