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Research paper details novel transfer learning for matrix estimation

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This research paper, titled "Low-Rank Plus Sparse Matrix Transfer Learning under Growing Representations and Ambient Dimensions," explores transfer learning techniques for structured matrix estimation. The paper proposes a framework where a source task is embedded as a subspace within a higher-dimensional target task, allowing for the estimation of low-dimensional innovations and sparse modifications. The authors developed an anchored alternating projection estimator and established error bounds that improve performance when rank and sparsity increments are small. The framework is applied to Markov transition matrix estimation and structured covariance estimation, with theoretical guarantees and empirical validation. AI

RANK_REASON The cluster contains a research paper detailing a novel transfer learning framework for matrix estimation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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Research paper details novel transfer learning for matrix estimation

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The cluster contains a research paper detailing a novel transfer learning framework for matrix estimation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jinhang Chai, Xuyuan Liu, Elynn Chen, Yujun Yan ·

    Low-Rank Plus Sparse Matrix Transfer Learning under Growing Representations and Ambient Dimensions

    arXiv:2601.21873v2 Announce Type: replace Abstract: Learning systems often expand their ambient features or latent representations over time, embedding earlier representations into larger spaces with limited new latent structure. We study transfer learning for structured matrix e…

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