1 citations · 3 across the 10 of their papers we have counts for
5 papers · 1 filter
Robust Filtering and Learning in State-Space Models: Skewness and Heavy Tails Via Asymmetric Laplace Distribution
Yifan Yu, Shengjie Xiu, Daniel P. Palomar
State-space models are pivotal for dynamic system analysis but often struggle with outlier data that deviates from Gaussian distributions, frequently exhibiting skewness and heavy…
Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches
Alexandre Hippert-Ferrer, Aude Sportisse, Amirhossein Javaheri +2
This tutorial aims to provide signal processing (SP) and machine learning (ML) practitioners with vital tools, in an accessible way, to answer the question: How to deal with missin…
Robust and Constrained Estimation of State-Space Models: A Majorization-Minimization Approach
Yifan Yu, Shengjie Xiu, Daniel P. Palomar
In this paper, we present a novel optimization algorithm designed specifically for estimating state-space models to deal with heavy-tailed measurement noise and constraints. Our al…
Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals
Andrei Buciulea, Jiaxi Ying, Antonio G. Marques +1
This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph structures from nodal signals. Our key contribution lies in modeling the signals as Gaussia…
Discerning and Enhancing the Weighted Sum-Rate Maximization Algorithms in Communications
Zepeng Zhang, Ziping Zhao, Kaiming Shen +2
Weighted sum-rate (WSR) maximization plays a critical role in communication system design. This paper examines three optimization methods for WSR maximization, which ensure converg…