Christoffel-DPS is a distribution-free optimal sensor placement framework for diffusion posterior sampling that provides non-asymptotic recovery bounds and outperforms Gaussian baselines on non-Gaussian benchmarks.
Numerische Mathematik , volume =
2 Pith papers cite this work, alongside 508 external citations. Polarity classification is still indexing.
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Iteris, an agentic research system, produced evidence and drafts for two open computational math problems that were verified after human correction.
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Christoffel-DPS: Optimal sensor placement in diffusion posterior sampling for arbitrary distributions
Christoffel-DPS is a distribution-free optimal sensor placement framework for diffusion posterior sampling that provides non-asymptotic recovery bounds and outperforms Gaussian baselines on non-Gaussian benchmarks.
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Iteris: Agentic Research Loops for Computational Mathematics
Iteris, an agentic research system, produced evidence and drafts for two open computational math problems that were verified after human correction.