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01/06/2023
A Levenberg-Marquardt Method for Nonsmooth Regularized Least Squares
We develop a Levenberg-Marquardt method for minimizing the sum of a smoo...
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05/01/2021
l1-Norm Minimization with Regula Falsi Type Root Finding Methods
Sparse level-set formulations allow practitioners to find the minimum 1-...
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08/24/2020
Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning
Data science, and machine learning in particular, is rapidly transformin...
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10/30/2019
Efficient Robust Parameter Identification in Generalized Kalman Smoothing Models
Dynamic inference problems in autoregressive (AR/ARMA/ARIMA), exponentia...
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10/15/2019
IRLS for Sparse Recovery Revisited: Examples of Failure and a Remedy
Compressed sensing is a central topic in signal processing with myriad a...
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09/24/2019
Trimmed Constrained Mixed Effects Models: Formulations and Algorithms
Mixed effects (ME) models inform a vast array of problems in the physica...
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06/25/2019
A unified sparse optimization framework to learn parsimonious physics-informed models from data
Machine learning (ML) is redefining what is possible in data-intensive f...
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07/14/2018
Sparse Relaxed Regularized Regression: SR3
Regularized regression problems are ubiquitous in statistical modeling, ...
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07/09/2018
Computer Assisted Localization of a Heart Arrhythmia
We consider the problem of locating a point-source heart arrhythmia usin...
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04/20/2018
Generalized Linear Model for Gamma Distributed Variables via Elastic Net Regularization
The Generalized Linear Model (GLM) for the Gamma distribution (glmGamma)...
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04/01/2018
Sparse Principal Component Analysis via Variable Projection
Sparse principal component analysis (SPCA) has emerged as a powerful tec...
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03/17/2018
Mean Reverting Portfolios via Penalized OU-Likelihood Estimation
We study an optimization-based approach to con- struct a mean-reverting ...
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03/07/2018
Fast Robust Methods for Singular State-Space Models
State-space models are used in a wide range of time series analysis form...
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07/31/2017
Learning Robust Representations for Computer Vision
Unsupervised learning techniques in computer vision often require learni...
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06/09/2017
Time Series Using Exponential Smoothing Cells
Time series analysis is used to understand and predict dynamic processes...
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06/06/2017
Shape Parameter Estimation
Performance of machine learning approaches depends strongly on the choic...
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08/08/2016
Boosting as a kernel-based method
Boosting combines weak (biased) learners to obtain effective learning al...
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07/09/2016
Beating level-set methods for 3D seismic data interpolation: a primal-dual alternating approach
Acquisition cost is a crucial bottleneck for seismic workflows, and low-...
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04/21/2016
Dynamic matrix factorization with social influence
Matrix factorization is a key component of collaborative filtering-based...
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03/01/2016
Dual Smoothing and Level Set Techniques for Variational Matrix Decomposition
We focus on the robust principal component analysis (RPCA) problem, and ...
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11/12/2015
Automatic Inference of the Quantile Parameter
Supervised learning is an active research area, with numerous applicatio...
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11/21/2014
Robust EM kernel-based methods for linear system identification
Recent developments in system identification have brought attention to r...
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03/26/2014
Beyond L2-Loss Functions for Learning Sparse Models
Incorporating sparsity priors in learning tasks can give rise to simple,...
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02/19/2014
Sparse Quantile Huber Regression for Efficient and Robust Estimation
We consider new formulations and methods for sparse quantile regression ...
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12/21/2013
Outlier robust system identification: a Bayesian kernel-based approach
In this paper, we propose an outlier-robust regularized kernel-based met...
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09/30/2013
Generalized system identification with stable spline kernels
Regularized least-squares approaches have been successfully applied to l...
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09/05/2013
Accelerating Hessian-free optimization for deep neural networks by implicit preconditioning and sampling
Hessian-free training has become a popular parallel second or- der optim...
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09/05/2013
Improvements to deep convolutional neural networks for LVCSR
Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Ne...
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09/05/2013
Semistochastic Quadratic Bound Methods
Partition functions arise in a variety of settings, including conditiona...
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06/05/2013
Fast Dual Variational Inference for Non-Conjugate LGMs
Latent Gaussian models (LGMs) are widely used in statistics and machine ...
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03/22/2013
Robust and Trend Following Student's t Kalman Smoothers
We present a Kalman smoothing framework based on modeling errors using t...
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03/12/2013
Linear system identification using stable spline kernels and PLQ penalties
The classical approach to linear system identification is given by param...
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03/08/2013
Optimization viewpoint on Kalman smoothing, with applications to robust and sparse estimation
In this paper, we present the optimization formulation of the Kalman fil...
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02/26/2013
Convex vs nonconvex approaches for sparse estimation: GLasso, Multiple Kernel Learning and Hyperparameter GLasso
The popular Lasso approach for sparse estimation can be derived via marg...
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02/20/2013
Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation
Recent SVD-free matrix factorization formulations have enabled rank mini...
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01/22/2013
The connection between Bayesian estimation of a Gaussian random field and RKHS
Reconstruction of a function from noisy data is often formulated as a re...
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01/19/2013
Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory
We introduce a class of quadratic support (QS) functions, many of which ...
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12/05/2012
Sparse seismic imaging using variable projection
We consider an important class of signal processing problems where the s...
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11/19/2012
Smoothing Dynamic Systems with State-Dependent Covariance Matrices
Kalman filtering and smoothing algorithms are used in many areas, includ...
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11/06/2011