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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.09708 |
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| _version_ | 1866913315485122560 |
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| author | Kowalczyk, Krzysztof Wachel, Paweł Rojas, Cristian R. |
| author_facet | Kowalczyk, Krzysztof Wachel, Paweł Rojas, Cristian R. |
| contents | This paper addresses a kernel-based learning problem for a network of agents locally observing a latent multidimensional, nonlinear phenomenon in a noisy environment. We propose a learning algorithm that requires only mild a priori knowledge about the phenomenon under investigation and delivers a model with corresponding non-asymptotic high probability error bounds. Both non-asymptotic analysis of the method and numerical simulation results are presented and discussed in the paper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09708 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Kernel-based learning with guarantees for multi-agent applications Kowalczyk, Krzysztof Wachel, Paweł Rojas, Cristian R. Multiagent Systems Machine Learning This paper addresses a kernel-based learning problem for a network of agents locally observing a latent multidimensional, nonlinear phenomenon in a noisy environment. We propose a learning algorithm that requires only mild a priori knowledge about the phenomenon under investigation and delivers a model with corresponding non-asymptotic high probability error bounds. Both non-asymptotic analysis of the method and numerical simulation results are presented and discussed in the paper. |
| title | Kernel-based learning with guarantees for multi-agent applications |
| topic | Multiagent Systems Machine Learning |
| url | https://arxiv.org/abs/2404.09708 |