In this paper we consider sparsity on a tensor level, as given by the n-rank of a tensor. In an
important sparse-vector approximation problem (compressed sensing) and the low-rank …

In this paper, we propose a novel Hessian inverse free Fully Single Loop Algorithm (FSLA)
for bilevel optimization problems. Classic algorithms for bilevel optimization admit a double …

Inverse problems in image processing are typically cast as optimization tasks, consisting of
data fidelity and stabilizing regularization terms. A recent regularization strategy of great …

In this paper we study convex bilevel optimization problems for which the inner level
consists of minimization of the sum of smooth and nonsmooth functions. The outer level aims …

The densification and expansion of wireless networks pose new challenges on energy
efficiency. With a drastic increase of infrastructure nodes (eg ultradense deployment of small …

In this paper, we propose the Bi-Sub-Gradient (Bi-SG) method, which is a generalization of
the classical sub-gradient method to the setting of convex bi-level optimization problems …

Bilevel Optimization has witnessed notable progress recently with new emerging efficient
algorithms. However, its application in the Federated Learning setting remains relatively …

Bilevel Optimization has witnessed notable progress recently with new emerging efficient
algorithms and has been applied to many machine learning tasks such as data cleaning …

Due to the hierarchical structure of many machine learning problems, bilevel programming
is becoming more and more important recently, however, the complicated correlation …

In this paper, we address the problem of reconstructing coverage maps from path-loss
measurements in cellular networks. We propose and evaluate two kernel-based adaptive …