September 2026

In decentralized optimization, gradient-tracking methods typically rely on a single global stepsize. This choice can be conservative when agents have local objectives with different smoothness constan

This paper primarily considers the robust estimation problem under Wasserstein distance constraints on the parameter and noise distributions in the linear measurement model with additive noise, which

Identifying the graphical structure underlying observed multivariate data is essential in numerous applications. Current methodologies are predominantly confined to deducing a single graph under the p

This article investigates the problem of deep learning-based state estimation for dynamic systems with partially known dynamics and unknown noise statistics. We propose a Deep Double-Bayesian Filter (

Compressive sensing (CS) enables fast spatial channel estimation in millimeter-wave and terahertz systems by leveraging the sparsity of the channel in the angle-domain. CS measurements, however, are o

This paper investigates the sparse phase retrieval problem, which aims to recover a sparse signal from a system of quadratic measurements. In this work, we propose a novel non-convex algorithm, termed

The Kalman Filter (KF) is used to estimate and predict state-space models. It frequently encounters uncertain covariates, such as those derived from noisy measurements of physical signals (e.g., senso

Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction. Various methods have been proposed to extend PCA to the union of subspace (UoS) setting for clustering d

An analytic para-Hermitian matrix is commonly used to characterize wideband signal models in array signal processing. In this paper, an algorithm for computing the analytic solution of a linear system

State-space models, while fundamental to dynamic system analysis, face significant challenges in handling non-Gaussian outliers characterized by skewness and heavy tails. This paper addresses robust s