mailitics

Tag: principal

  • A brief note on learning problem with global perspectives

    A brief note on learning problem with global perspectives arXiv:2601.05441v1 Announce Type: new Abstract: This brief note considers the problem of learning with dynamic-optimizing principal-agent setting, in which the agents are allowed to have global perspectives about the learning process, i.e., the ability to view things according to their relative importances or in their true…

  • Calibrated Principal Component Regression

    Calibrated Principal Component Regression arXiv:2510.19020v1 Announce Type: new Abstract: We propose a new method for statistical inference in generalized linear models. In the overparameterized regime, Principal Component Regression (PCR) reduces variance by projecting high-dimensional data to a low-dimensional principal subspace before fitting. However, PCR incurs truncation bias whenever the true regression vector has mass outside…

  • Identifying Neural Signatures from fMRI using Hybrid Principal Components Regression

    Identifying Neural Signatures from fMRI using Hybrid Principal Components Regression arXiv:2509.07300v1 Announce Type: new Abstract: Recent advances in neuroimaging analysis have enabled accurate decoding of mental state from brain activation patterns during functional magnetic resonance imaging scans. A commonly applied tool for this purpose is principal components regression regularized with the least absolute shrinkage and…

  • Day to day work at lead/principal data scientist

    Day to day work at lead/principal data scientist Hi, I have 9 years of experience in ml/dl. I have been looking for a role in lead/principal ds. Can you tell me what expectations do you guys face at the role. Data science knowledge? Ml ops knowledge? Team management? submitted by /u/sourabharsh [link] [comments] /u/sourabharsh Go…

  • Riemannian Principal Component Analysis

    Riemannian Principal Component Analysis arXiv:2506.00226v1 Announce Type: new Abstract: This paper proposes an innovative extension of Principal Component Analysis (PCA) that transcends the traditional assumption of data lying in Euclidean space, enabling its application to data on Riemannian manifolds. The primary challenge addressed is the lack of vector space operations on such manifolds. Fletcher et…