Kernel Mean Embedding Topology: Weak and Strong Forms for Stochastic Kernels and Implications for Model Learning Article Swipe

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YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2502.13486

We introduce a novel topology, called Kernel Mean Embedding Topology, for stochastic kernels, in a weak and strong form. This topology, defined on the spaces of Bochner integrable functions from a signal space to a space of probability measures endowed with a Hilbert space structure, allows for a versatile formulation. This construction allows one to obtain both a strong and weak formulation. (i) For its weak formulation, we highlight the utility on relaxed policy spaces, and investigate connections with the Young narrow topology and Borkar (or \( w^* \))-topology, and establish equivalence properties. We report that, while both the \( w^* \)-topology and kernel mean embedding topology are relatively compact, they are not closed. Conversely, while the Young narrow topology is closed, it lacks relative compactness. (ii) We show that the strong form provides an appropriate formulation for placing topologies on spaces of models characterized by stochastic kernels with explicit robustness and learning theoretic implications on optimal stochastic control under discounted or average cost criteria. (iii) We thus show that this topology possesses several properties making it ideal to study optimality and approximations (under the weak formulation) and robustness (under the strong formulation) for many applications.

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Embedding Kernel (algebra) Topology (electrical circuits) Mathematics Computer science Artificial intelligence Combinatorics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2502.13486
PDF: https://arxiv.org/pdf/2502.13486
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4407764334

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DOI: https://doi.org/10.48550/arxiv.2502.13486

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Title: Kernel Mean Embedding Topology: Weak and Strong Forms for Stochastic Kernels and Implications for Model Learning

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-02-19

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Authors: Naci Saldı, Serdar Yüksel

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Landing page: https://arxiv.org/abs/2502.13486

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PDF URL: https://arxiv.org/pdf/2502.13486

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Concepts: Embedding, Kernel (algebra), Topology (electrical circuits), Mathematics, Computer science, Artificial intelligence, Combinatorics

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