paper-with-me

홈 › Papers

Field Codes for Distributed Coupling Samplers and Certified Empirical Transport

2026-07-29 · Hung Mai, Hai Nguyen, Luong Doan, Ngoc Vu, Khanh Nguyen, Nhung Duong, Tuan Do arxiv

In this paper, we formulate three communication tasks for empirical optimal transport: distributed coupling sampling, cost-evaluable coupling output, and scalar value-certified sampling. Our main result is a field-code compiler: any communicated transport field approximating an optimal empirical Monge map to error $η$ can be completed by sparse target-cell residuals into an exact-marginal value-certified sampler with scalar certificate $W_1(μ,ν)\leq U\leq W_1(μ,ν)+2Δ$, where $Δ$ is the public target-partition diameter. The certificate accuracy is controlled by $Δ$ alone. The field error $η$ controls residual communication under a cell-margin condition; without a margin, $η$ alone does not bound residuals. We instantiate the compiler via adaptive local-affine and tensor-product spline codes with $d(m+1)^db$ field bits in the spline case, plus residual lists charged separately. For lower bounds, exact Gap-Hamming embeddings prove certified output is hard, including a smooth cell-packing diffeomorphism family requiring $Ω(\varepsilon^{-2d/(d+4)})$ communication for any cost-evaluable, cost-certified, or value-certified protocol. The same gadgets admit zero-communication samplers, formally separating the sampler and certificate-bearing output models. These results identify the transport field as the right communicated object whenever a field code is available, primarily as a residual-sparsity tool.

📄 PDF Abstract BibTeX arXiv:2607.27078

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Load--Reserve Wasserstein Propagation for Isotropic Diffusion Samplers

2026-03-20 · Zicheng Lyu, Zengfeng Huang arxiv

Many Wasserstein analyses of diffusion samplers control reverse-time propagation by global stability summaries of the learned drift. These summaries can hide radial geometry: equal-height expansive regions of different w…

Weight-Space Physics: Interpretable Hypernetworks for Lattice Quantum Field Theories

2026-07-08 · Tobias Göbel, Julian R. Ebelt, Zier Mensch, Mathis Gerdes 외 arxiv

Lattice field theory is the workhorse of non-perturbative physics, used to simulate phenomena from the strong nuclear force to critical phenomena in materials. Its Boltzmann distributions are parametrized analytically by…

Interdependent Gibbs Samplers

2018-04-11 · Mark Kozdoba, Shie Mannor

Gibbs sampling, as a model learning method, is known to produce the most accurate results available in a variety of domains, and is a de facto standard in these domains. Yet, it is also well known that Gibbs random walks…

Reducing Certified Regression to Certified Classification for General Poisoning Attacks

2022-08-29 · Zayd Hammoudeh, Daniel Lowd

Adversarial training instances can severely distort a model's behavior. This work investigates certified regression defenses, which provide guaranteed limits on how much a regressor's prediction may change under a poison…

Classificationregression

NeuMC -- a package for neural sampling for lattice field theories

2025-03-14 · Piotr Bialas, Piotr Korcyl, Tomasz Stebel, Dawid Zapolski

We present the \texttt{NeuMC} software package, based on \pytorch, aimed at facilitating the research on neural samplers in lattice field theories. Neural samplers based on normalizing flows are becoming increasingly pop…