Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
PoE-Bridge uses a product-of-experts bridge between diffusion and autoregressive distributions, with DLM drafting plus rejection and importance sampling, to deliver 5x speedup over standard DLM decoding while recovering at least 95% of AR performance on math and coding tasks.
Mass ratio reversals produce qualitatively different contributions to BBH merger rates and masses in COMPAS versus SEVN simulations, with core-growth dominating and most systems arising from massive low-metallicity progenitors.
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
citing papers explorer
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Error Bounds for Importance Sampling with Estimated Proposal Distributions
Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.
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Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge
PoE-Bridge uses a product-of-experts bridge between diffusion and autoregressive distributions, with DLM drafting plus rejection and importance sampling, to deliver 5x speedup over standard DLM decoding while recovering at least 95% of AR performance on math and coding tasks.
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Massquerade: Impacts of Mass Ratio Reversals on Binary Black Hole Merger Rates and Mass Distributions
Mass ratio reversals produce qualitatively different contributions to BBH merger rates and masses in COMPAS versus SEVN simulations, with core-growth dominating and most systems arising from massive low-metallicity progenitors.
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Reliable model selection in the presence of parameter non-identifiability
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.