Results 151 to 160 of about 2,509,053 (235)
Parameter estimation from aggregate observations: a Wasserstein distance-based sequential Monte Carlo sampler. [PDF]
R Soc Open Sci, 2023 Cheng C, Wen L, Li J.
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We establish upper bounds for the expected Gaussian-smoothed $p$-Wasserstein distance between a probability measure $μ$ and the corresponding empirical measure $μ_N$, whenever $μ$ has finite $q$-th moments for any $q>p$. This generalizes recent results that were valid only for $q>2p+2d$. We provide two distinct proofs of such a result.
Cosso, Andrea +2 more
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On the Mean‐Field Limit of Consensus‐Based Methods
Mathematical Methods in the Applied Sciences, Volume 49, Issue 5, Page 4214-4240, 30 March 2026.ABSTRACT Consensus‐based optimization (CBO) employs a swarm of particles evolving as a system of stochastic differential equations (SDEs). Recently, it has been adapted to yield a derivative free sampling method referred to as consensus‐based sampling (CBS). In this paper, we investigate the “mean‐field limit” of a class of consensus methods, including
Marvin Koß, Simon Weissmann, Jakob Zech
wiley +1 more source
Skorohod Representation Theorem Via Disintegrations [PDF]
Let (µn : n >= 0) be Borel probabilities on a metric space S such that µn -> µ0 weakly. Say that Skorohod representation holds if, on some probability space, there are S-valued random variables Xn satisfying Xn - µn for all n and Xn -> X0 in probability.
Luca Pratelli +2 more
core
Hybrid Wasserstein Distance: An Approximation for Optimal Transport Distances
ComputationProjection-based variants of optimal transport, such as the Sliced Wasserstein (SW) and its extensions, have become popular alternatives to classical Wasserstein distances due to their scalability and analytical tractability.
Sara Nassar +2 more
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The Wasserstein Distance for Ricci Shrinkers
International Mathematics Research NoticesAbstract Let $(M^{n},g,f)$ be a Ricci shrinker such that $\text{Ric}_{f}=\frac{1}{2}g$ and the measure induced by the weighted volume element $(4\pi )^{-\frac{n}{2}}e^{-f}dv_{g}$ is a probability measure. Given a point $p\in M$, we consider two probability measures defined in the tangent space $T_{p}M$, namely the Gaussian measure ...
Conrado, Franciele, Zhou, Detang
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IEEE AccessMotivation: Accurate prediction of protein subcellular localization (PSL) from sequence is central to cell biology and proteome-scale annotation. However, current approaches face a persistent trade-off: deep learning models often deliver strong accuracy ...
Jiayang Xu, Yangzhou Chen, Xin Chen
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Optimal Estimation of Wasserstein Distance on A Tree with An Application to Microbiome Studies. [PDF]
J Am Stat Assoc, 2021 Wang S, Cai TT, Li H.
europepmc +1 more source
Wasserstein Distances, Neuronal Entanglement, and Sparsity
Disentangling polysemantic neurons is at the core of many current approaches to interpretability of large language models. Here we attempt to study how disentanglement can be used to understand performance, particularly under weight sparsity, a leading post-training optimization technique. We suggest a novel measure for estimating neuronal entanglement:
Sawmya, Shashata +4 more
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Journal of Marine Science and EngineeringPolymetallic nodules are spherical or ellipsoidal mineral aggregates formed naturally in deep-sea environments. They contain a variety of metallic elements and are important solid mineral resources on the seabed.
Kai Sun +6 more
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