arXiv:2511. 11927v2 Announce Type: replace-cross Abstract: Principal Component Analysis (PCA) is a standard tool for extracting a low-rank signal from noisy observations.
By Urte Adomaityte, Gabriele Sicuro, Pierpaolo Vivo
arXiv:2609.28163v1 Announce Type: cross
Abstract: We study the high-dimensional recovery of a signal vector $\mathbf{x}$ in the presence of sparse Wishart-like noise. We define an $N \times N$ matrix...
By Preben Forer, Urte Adomaityte, Pierpaolo Vivo
arXiv:2609. 17048v1 Announce Type: cross Abstract: We study nonconvex methods for matrix completion, the problem of recovering a low-rank matrix from a subset of its entries.
By Jian-Feng Cai, Xiliang Lu, Juntao You
arXiv:2412. 16457v3 Announce Type: replace-cross Abstract: In this paper, we focus on the matching recovery problem between a pair of correlated Gaussian Wigner matrices with a latent vertex correspondence.
By Zhangsong Li
arXiv:2602. 20376v3 Announce Type: replace-cross Abstract: We study the problem of maximizing a complex-valued quadratic form over the $K^{\text{th}}$ roots of unity.
By Ria Stevens, Fangshuo Liao, Barbara Su, Thanasis Hadjidimoulas, Jianqiang Li, Anastasios Kyrillidis
arXiv:2607. 00252v1 Announce Type: new Abstract: We present an algorithm for the group distributionally robust (GDR) least squares problem.
By Naren Sarayu Manoj, Kumar Kshitij Patel