Daniel P. Palomar
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View article: Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches
Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches Open
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 missing data? There are many strategies to handle incomplete sign…
View article: High-dimensional false discovery rate control for dependent variables
High-dimensional false discovery rate control for dependent variables Open
Algorithms that ensure reproducible findings from large-scale, high-dimensional data are pivotal in numerous signal processing applications. In recent years, multivariate false discovery rate (FDR) controlling methods have emerged, providi…
View article: The terminating-random experiments selector: Fast high-dimensional variable selection with false discovery rate control
The terminating-random experiments selector: Fast high-dimensional variable selection with false discovery rate control Open
We propose the Terminating-Random Experiments (T-Rex) selector, a fast variable selection method for high-dimensional data. The T-Rex selector controls a user-defined target false discovery rate (FDR) while maximizing the number of selecte…
View article: Polynomial Graphical Lasso: Learning Edges From Gaussian Graph-Stationary Signals
Polynomial Graphical Lasso: Learning Edges From Gaussian Graph-Stationary Signals Open
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 Gaussian and stationary on the graph, enabling the development of …
View article: Time-Varying Graph Learning for Data with Heavy-Tailed Distribution
Time-Varying Graph Learning for Data with Heavy-Tailed Distribution Open
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 to model the dynamic interactions between entities in such…
View article: Robust and Constrained Estimation of State-Space Models: A Majorization-Minimization Approach
Robust and Constrained Estimation of State-Space Models: A Majorization-Minimization Approach Open
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 algorithm addresses two significant limitations found in exis…
View article: False Discovery Rate Control for Fast Screening of Large-Scale Genomics Biobanks
False Discovery Rate Control for Fast Screening of Large-Scale Genomics Biobanks Open
Genomics biobanks are information treasure troves with thousands of phenotypes (e.g., diseases, traits) and millions of single nucleotide polymorphisms (SNPs). The development of methodologies that provide reproducible discoveries is essen…
View article: The Informed Elastic Net for Fast Grouped Variable Selection and FDR Control in Genomics Research
The Informed Elastic Net for Fast Grouped Variable Selection and FDR Control in Genomics Research Open
Modern genomics research relies on genome-wide association studies (GWAS) to identify the few genetic variants among potentially millions that are associated with diseases of interest. Only reproducible discoveries of groups of association…
View article: Seasonal antigenic prediction of influenza A H3N2 using machine learning
Seasonal antigenic prediction of influenza A H3N2 using machine learning Open
Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vac…
View article: Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals
Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals Open
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 Gaussian and stationary on the graph, enabling the development of …
View article: High-Dimensional False Discovery Rate Control for Dependent Variables
High-Dimensional False Discovery Rate Control for Dependent Variables Open
Algorithms that ensure reproducible findings from large-scale, high-dimensional data are pivotal in numerous signal processing applications. In recent years, multivariate false discovery rate (FDR) controlling methods have emerged, providi…
View article: FDR-Controlled Portfolio Optimization for Sparse Financial Index Tracking
FDR-Controlled Portfolio Optimization for Sparse Financial Index Tracking Open
In high-dimensional data analysis, such as financial index tracking or biomedical applications, it is crucial to select the few relevant variables while maintaining control over the false discovery rate (FDR). In these applications, strong…
View article: Sparse PCA with False Discovery Rate Controlled Variable Selection
Sparse PCA with False Discovery Rate Controlled Variable Selection Open
Sparse principal component analysis (PCA) aims at mapping large dimensional data to a linear subspace of lower dimension. By imposing loading vectors to be sparse, it performs the double duty of dimension reduction and variable selection. …
View article: Joint Signal Recovery and Graph Learning from Incomplete Time-Series
Joint Signal Recovery and Graph Learning from Incomplete Time-Series Open
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, which might not be available in some scenarios. In this wo…
View article: Discerning and Enhancing the Weighted Sum-Rate Maximization Algorithms in Communications
Discerning and Enhancing the Weighted Sum-Rate Maximization Algorithms in Communications Open
Weighted sum-rate (WSR) maximization plays a critical role in communication system design. This paper examines three optimization methods for WSR maximization, which ensure convergence to stationary points: two block coordinate ascent (BCA…
View article: Learning Large-Scale MTP$_2$ Gaussian Graphical Models via Bridge-Block Decomposition
Learning Large-Scale MTP$_2$ Gaussian Graphical Models via Bridge-Block Decomposition Open
This paper studies the problem of learning the large-scale Gaussian graphical models that are multivariate totally positive of order two ($\text{MTP}_2$). By introducing the concept of bridge, which commonly exists in large-scale sparse gr…
View article: Seasonal antigenic prediction of influenza A H3N2 using machine learning
Seasonal antigenic prediction of influenza A H3N2 using machine learning Open
Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vac…
View article: Affine equivariant Tyler's M-estimator applied to tail parameter learning of elliptical distributions
Affine equivariant Tyler's M-estimator applied to tail parameter learning of elliptical distributions Open
We propose estimating the scale parameter (mean of the eigenvalues) of the scatter matrix of an unspecified elliptically symmetric distribution using weights obtained by solving Tyler's M-estimator of the scatter matrix. The proposed Tyler…
View article: Affine Equivariant Tyler's M-Estimator Applied to Tail Parameter Learning of Elliptical Distributions
Affine Equivariant Tyler's M-Estimator Applied to Tail Parameter Learning of Elliptical Distributions Open
We propose estimating the scale parameter (mean of the eigenvalues) of the scatter matrix of an unspecified elliptically symmetric distribution using weights obtained by solving Tyler's M-estimator of the scatter matrix. The proposed Tyler…
View article: A Fast Successive QP Algorithm for General Mean-Variance Portfolio Optimization
A Fast Successive QP Algorithm for General Mean-Variance Portfolio Optimization Open
The mean and variance of portfolio returns are the standard quantities to measure the expected return and risk of a portfolio. Efficient portfolios that provide optimal trade-offs between mean and variance warrant consideration. To express…
View article: Adaptive Estimation of Graphical Models under Total Positivity
Adaptive Estimation of Graphical Models under Total Positivity Open
We consider the problem of estimating (diagonally dominant) M-matrices as precision matrices in Gaussian graphical models. These models exhibit intriguing properties, such as the existence of the maximum likelihood estimator with merely tw…
View article: Efficient Algorithms for General Isotone Optimization
Efficient Algorithms for General Isotone Optimization Open
Monotonicity is often a fundamental assumption involved in the modeling of a number of real-world applications. From an optimization perspective, monotonicity is formulated as partial order constraints among the optimization variables, com…
View article: Efficient and Scalable Parametric High-Order Portfolios Design via the Skew-t Distribution
Efficient and Scalable Parametric High-Order Portfolios Design via the Skew-t Distribution Open
Since Markowitz's mean-variance framework, optimizing a portfolio that maximizes the profit and minimizes the risk has been ubiquitous in the financial industry. Initially, profit and risk were measured by the first two moments of the port…
View article: Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity
Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity Open
We study the problem of estimating precision matrices in Gaussian distributions that are multivariate totally positive of order two ($\mathrm{MTP}_2$). The precision matrix in such a distribution is an M-matrix. This problem can be formula…
View article: The Terminating-Knockoff Filter: Fast High-Dimensional Variable Selection with False Discovery Rate Control
The Terminating-Knockoff Filter: Fast High-Dimensional Variable Selection with False Discovery Rate Control Open
We propose the Terminating-Knockoff (T-Knock) filter, a fast variable selection method for high-dimensional data. The T-Knock filter controls a user-defined target false discovery rate (FDR) while maximizing the number of selected true pos…
View article: The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control
The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control Open
We propose the Terminating-Random Experiments (T-Rex) selector, a fast variable selection method for high-dimensional data. The T-Rex selector controls a user-defined target false discovery rate (FDR) while maximizing the number of selecte…
View article: Improved estimation of the degree of freedom parameter of multivariate $t$-distribution
Improved estimation of the degree of freedom parameter of multivariate $t$-distribution Open
The multivariate t (MVT)-distribution is a widely used statistical model in various application domains, mainly due to its adaptability to heavy-tailed data. However, estimating the degree of freedom (d.o.f) parameter, that controls the sh…
View article: Majorization-Minimization on the Stiefel Manifold With Application to Robust Sparse PCA
Majorization-Minimization on the Stiefel Manifold With Application to Robust Sparse PCA Open
This paper proposes a framework for optimizing cost functions of orthonormal basis learning problems, such as principal component analysis (PCA), subspace recovery, orthogonal dictionary learning, etc. The optimization algorithm is derived…
View article: Algorithms for Learning Graphs in Financial Markets
Algorithms for Learning Graphs in Financial Markets Open
In the past two decades, the field of applied finance has tremendously benefited from graph theory. As a result, novel methods ranging from asset network estimation to hierarchical asset selection and portfolio allocation are now part of p…
View article: Learning Undirected Graphs in Financial Markets
Learning Undirected Graphs in Financial Markets Open
We investigate the problem of learning undirected graphical models under Laplacian structural constraints from the point of view of financial market data. We show that Laplacian constraints have meaningful physical interpretations related …