Hilbert Space Embeddings
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Quantum Maximum Likelihood Prediction via Hilbert Space Embeddings
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Continuum-marginal optimal transport: a mesh-free kernel method
arXiv:2604.24226v2 Announce Type: replace-cross Abstract: In this paper we study continuum-marginal optimal transport. Given a time-continuous family of probability marginals, the problem is to recover the minimum-energy velocity field whose flow reproduces every marginal. This problem is the continuum limit of the classical two-marginal Benamou--Brenier formulation, and also the deterministic limit of the Nelson problem of stochastic optimal transport.
OnlyDense: Reduced-Order Modeling for Lagrangian simulation
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Row-Stochastic Matrices Can Provably Outperform Doubly Stochastic Matrices in Decentralized Learning
arXiv:2511.19513v3 Announce Type: replace Abstract: Decentralized learning often involves a weighted global loss with heterogeneous node weights $\lambda$. We revisit two natural strategies for incorporating these weights: (i) embedding them into the local losses to retain a uniform weight (and thus a doubly stochastic matrix), and (ii) keeping the original losses while employing a $\lambda$-induced row-stochastic matrix. Although prior work shows that both strategies target the same...
SHAP-Guided Kernel Actor-Critic for Explainable Reinforcement Learning
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