3 papers
math.OC2025
Bilevel Learning via Inexact Stochastic Gradient Descent
Mohammad Sadegh Salehi, Subhadip Mukherjee, Lindon Roberts +1
Bilevel optimization is a central tool in machine learning for high-dimensional hyperparameter tuning. Its applications are vast; for instance, in imaging it can be used for learni…
math.OC2025
A primal-dual algorithm for image reconstruction with input-convex neural network regularizers
Matthias J. Ehrhardt, Subhadip Mukherjee, Hok Shing Wong
We address the optimization problem in a data-driven variational reconstruction framework, where the regularizer is parameterized by an input-convex neural network (ICNN). While gr…
math.OC2025
Bilevel Learning with Inexact Stochastic Gradients
Mohammad Sadegh Salehi, Subhadip Mukherjee, Lindon Roberts +1
Bilevel learning has gained prominence in machine learning, inverse problems, and imaging applications, including hyperparameter optimization, learning data-adaptive regularizers,…