30 citations · 127 across the 18 of their papers we have counts for
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On Asymptotic Linear Convergence Rate of Iterative Hard Thresholding for Matrix Completion
Trung Vu, Evgenia Chunikhina, Raviv Raich
Iterative hard thresholding (IHT) has gained in popularity over the past decades in large-scale optimization. However, convergence properties of this method have only been explored…
On Asymptotic Linear Convergence of Projected Gradient Descent for Constrained Least Squares
Trung Vu, Raviv Raich
Many recent problems in signal processing and machine learning such as compressed sensing, image restoration, matrix/tensor recovery, and non-negative matrix factorization can be c…
A Closed-Form Bound on the Asymptotic Linear Convergence of Iterative Methods via Fixed Point Analysis
Trung Vu, Raviv Raich
In many iterative optimization methods, fixed-point theory enables the analysis of the convergence rate via the contraction factor associated with the linear approximation of the f…
Active Learning in Incomplete Label Multiple Instance Multiple Label Learning
Tam Nguyen, Raviv Raich
In multiple instance multiple label learning, each sample, a bag, consists of multiple instances. To alleviate labeling complexity, each sample is associated with a set of bag-leve…
Exact Linear Convergence Rate Analysis for Low-Rank Symmetric Matrix Completion via Gradient Descent
Trung Vu, Raviv Raich
Factorization-based gradient descent is a scalable and efficient algorithm for solving low-rank matrix completion. Recent progress in structured non-convex optimization has offered…