33 citations · 92 across the 19 of their papers we have counts for
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math.OC2022
Robust Data-Driven Accelerated Mirror Descent
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +2
Learning-to-optimize is an emerging framework that leverages training data to speed up the solution of certain optimization problems. One such approach is based on the classical mi…
math.OC2022
Data-Driven Mirror Descent with Input-Convex Neural Networks
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +1
Learning-to-optimize is an emerging framework that seeks to speed up the solution of certain optimization problems by leveraging training data. Learned optimization solvers have be…
math.OC2022★ 1 cited
Equivariance Regularization for Image Reconstruction
Junqi Tang
In this work, we propose Regularization-by-Equivariance (REV), a novel structure-adaptive regularization scheme for solving imaging inverse problems under incomplete measurements.…