研究人员开发了一种新颖的低秩张量补全算法，该算法使用交替方向乘子法（ADMM）优化框架扩展了矩阵补全技术。这种新方法将问题重新表述为通过迭代求解的子问题，并结合了超松弛和自适应惩罚参数以提高收敛性和性能。此外，还提出了一种名为 MAlocate 的新多任务主动学习算法，用于同时解决多个矩阵补全问题，该算法能够适应未知的矩阵秩并展示出 minimax 最优性。 AI
影响 这些论文介绍了张量和矩阵补全的新算法方法，有可能改进各种机器学习应用中的数据插补和分析。
排序理由 提出了两篇不同的 arXiv 论文，一篇关于通过 ADMM 进行低秩张量补全，另一篇关于主动多矩阵补全。
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arXiv:2508.00091v3 Announce Type: replace-cross Abstract: The problem of recovering the configuration of points from their partial pairwise distances, referred to as the Euclidean Distance Matrix Completion (EDMC) problem, arises in a broad range of applications, including sensor…
arXiv:2605.03736v1 Announce Type: cross Abstract: We consider a novel algorithm, for the completion of partially observed low-rank tensors, as a generalization of matrix completion. The proposed low-rank tensor completion (TC) method builds on the conventional nuclear norm (NN) m…
We consider a novel algorithm, for the completion of partially observed low-rank tensors, as a generalization of matrix completion. The proposed low-rank tensor completion (TC) method builds on the conventional nuclear norm (NN) minimization-based low-rank TC paradigm, by leverag…
arXiv:2605.02458v1 Announce Type: new Abstract: In this work, we formulate a new multi-task active learning setting in which the learner's goal is to solve multiple matrix completion problems simultaneously. At each round, the learner can choose from which matrix it receives a sa…
In this work, we formulate a new multi-task active learning setting in which the learner's goal is to solve multiple matrix completion problems simultaneously. At each round, the learner can choose from which matrix it receives a sample from an entry drawn uniformly at random. Ou…