1 citations · 3 across the 10 of their papers we have counts for
10 papers
Virtual Dummies: Enabling Scalable FDR-Controlled Variable Selection via Sequential Sampling of Null Features
Taulant Koka, Jasin Machkour, Daniel P. Palomar +1
High-dimensional variable selection, particularly in genomics, requires error-controlling procedures that scale to millions of predictors. The Terminating-Random Experiments (T-Rex…
Learning False Discovery Rate Control via Model-Based Neural Networks
Arnau Vilella, Jasin Machkour, Michael Muma +1
Controlling the false discovery rate (FDR) in high-dimensional variable selection requires balancing rigorous error control with statistical power. Existing methods with provable g…
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…
Clustering of Incomplete Data via a Bipartite Graph Structure
Amirhossein Javaheri, Daniel P. Palomar
There are various approaches to graph learning for data clustering, incorporating different spectral and structural constraints through diverse graph structures. Some methods rely…
Time-Varying Graph Learning for Data with Heavy-Tailed Distribution
Amirhossein Javaheri, Jiaxi Ying, Daniel P. Palomar +1
Graph models provide efficient tools to capture the underlying structure of data defined over networks. Many real-world network topologies are subject to change over time. Learning…
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…
Joint Signal Recovery and Graph Learning from Incomplete Time-Series
Amirhossein Javaheri, Arash Amini, Farokh Marvasti +1
Learning a graph from data is the key to taking advantage of graph signal processing tools. Most of the conventional algorithms for graph learning require complete data statistics,…
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…