Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation

1 code implementation • 27 Mar 2024 • Yanhao Jin, Krishnakumar Balasubramanian, Debashis Paul

Finally, we propose and analyze an estimator of the inverse covariance matrix of random regression coefficients based on data from the training tasks.

Meta-Learning regression +1

Sparse Equisigned PCA: Algorithms and Performance Bounds in the Noisy Rank-1 Setting

no code implementations • 22 May 2019 • Arvind Prasadan, Raj Rao Nadakuditi, Debashis Paul

Singular value decomposition (SVD) based principal component analysis (PCA) breaks down in the high-dimensional and limited sample size regime below a certain critical eigen-SNR that depends on the dimensionality of the system and the number of samples.