May 14, 2024, 4:42 a.m. | Ruikai Yang, Fan He, Mingzhen He, Jie Yang, Xiaolin Huang

cs.LG updates on

arXiv:2405.07791v1 Announce Type: new
Abstract: Random feature (RF) has been widely used for node consistency in decentralized kernel ridge regression (KRR). Currently, the consistency is guaranteed by imposing constraints on coefficients of features, necessitating that the random features on different nodes are identical. However, in many applications, data on different nodes varies significantly on the number or distribution, which calls for adaptive and data-dependent methods that generate different RFs. To tackle the essential difficulty, we propose a new decentralized KRR …

abstract applications arxiv constraints cs.dc cs.lg data decentralized feature features however kernel node nodes random regression ridge type

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