In recent years there has been increasing interest in leveraging denoisers for solving
general inverse problems. Two leading frameworks are regularization-by-denoising (RED) …

DC (difference of convex functions) programming and DC algorithm (DCA) are powerful
tools for nonsmooth nonconvex optimization. This field was created in 1985 by Pham Dinh …

The past decade has seen the rapid growth of model based image reconstruction (MBIR)
algorithms, which are often applications or adaptations of convex optimization algorithms …

Mathematical optimization has always been at the heart of engineering, statistics, and
economics. In these applied domains, optimization concepts and methods have often been …

We discuss two key problems related to learning and optimization of neural networks: the
computation of the adversarial attack for adversarial robustness and approximate …

Over-the-air computation (AirComp) is a disruptive technique for fast wireless data
aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition …

Unsupervised classification is a highly important task of machine learning methods.
Although achieving great success in supervised classification, support vector machine …

Recent works have revealed an essential paradigm in designing loss functions that
differentiate individual losses versus aggregate losses. The individual loss measures the …

The boosted difference of convex functions algorithm (BDCA) was recently proposed for
minimizing smooth difference of convex (DC) functions. BDCA accelerates the convergence …

In this paper, we analyze Difference of Convex Neural Networks in the context of one-
dimensional nonlinear regression. Specifically, we show the surprising ability of the …