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Tag: subspace

  • Perturbations in the Orthogonal Complement Subspace for Efficient Out-of-Distribution Detection

    Perturbations in the Orthogonal Complement Subspace for Efficient Out-of-Distribution Detection arXiv:2511.00849v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is essential for deploying deep learning models in open-world environments. Existing approaches, such as energy-based scoring and gradient-projection methods, typically rely on high-dimensional representations to separate in-distribution (ID) and OOD samples. We introduce P-OCS (Perturbations in the…

  • Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective

    Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective arXiv:2505.14808v1 Announce Type: new Abstract: This work aims to demystify the out-of-distribution (OOD) capabilities of in-context learning (ICL) by studying linear regression tasks parameterized with low-rank covariance matrices. With such a parameterization, we can model distribution shifts as a varying angle between the subspace of the…

  • Subspace Recovery in Winsorized PCA: Insights into Accuracy and Robustness

    Subspace Recovery in Winsorized PCA: Insights into Accuracy and Robustness arXiv:2502.16391v1 Announce Type: new Abstract: In this paper, we explore the theoretical properties of subspace recovery using Winsorized Principal Component Analysis (WPCA), utilizing a common data transformation technique that caps extreme values to mitigate the impact of outliers. Despite the widespread use of winsorization in…

  • Estimating shared subspace with AJIVE: the power and limitation of multiple data matrices

    Estimating shared subspace with AJIVE: the power and limitation of multiple data matrices arXiv:2501.09336v1 Announce Type: new Abstract: Integrative data analysis often requires disentangling joint and individual variations across multiple datasets, a challenge commonly addressed by the Joint and Individual Variation Explained (JIVE) model. While numerous methods have been developed to estimate the shared subspace…

  • Preconditioned Subspace Langevin Monte Carlo

    Preconditioned Subspace Langevin Monte Carlo arXiv:2412.13928v1 Announce Type: new Abstract: We develop a new efficient method for high-dimensional sampling called Subspace Langevin Monte Carlo. The primary application of these methods is to efficiently implement Preconditioned Langevin Monte Carlo. To demonstrate the usefulness of this new method, we extend ideas from subspace descent methods in Euclidean…