14 citations · 20 across the 16 of their papers we have counts for
3 papers · 1 filter
Smooth optimization using global and local low-rank regularizers
Rodrigo A. Lobos, Javier Salazar Cavazos, Raj Rao Nadakuditi +1
Many inverse problems and signal processing problems involve low-rank regularizers based on the nuclear norm. Commonly, proximal gradient methods (PGM) are adopted to solve this ty…
FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation
Siyi Chen, Yixuan Jia, Qing Qu +2
Data assimilation (DA) integrates observations with a dynamical model to estimate states of PDE-governed systems. Model-driven methods (e.g., Kalman, particle) presuppose full know…
Streaming Heteroscedastic Probabilistic PCA with Missing Data
Kyle Gilman, David Hong, Jeffrey A. Fessler +1
Streaming principal component analysis (PCA) is an integral tool in large-scale machine learning for rapidly estimating low-dimensional subspaces from very high-dimensional data ar…