
DataDriven Aerospace Engineering: Reframing the Industry with Machine Learning
Data science, and machine learning in particular, is rapidly transformin...
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Efficient Robust Parameter Identification in Generalized Kalman Smoothing Models
Dynamic inference problems in autoregressive (AR/ARMA/ARIMA), exponentia...
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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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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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A unified sparse optimization framework to learn parsimonious physicsinformed models from data
Machine learning (ML) is redefining what is possible in dataintensive f...
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Sparse Relaxed Regularized Regression: SR3
Regularized regression problems are ubiquitous in statistical modeling, ...
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Computer Assisted Localization of a Heart Arrhythmia
We consider the problem of locating a pointsource heart arrhythmia usin...
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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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Sparse Principal Component Analysis via Variable Projection
Sparse principal component analysis (SPCA) has emerged as a powerful tec...
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Mean Reverting Portfolios via Penalized OULikelihood Estimation
We study an optimizationbased approach to con struct a meanreverting ...
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Fast Robust Methods for Singular StateSpace Models
Statespace models are used in a wide range of time series analysis form...
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Learning Robust Representations for Computer Vision
Unsupervised learning techniques in computer vision often require learni...
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Time Series Using Exponential Smoothing Cells
Time series analysis is used to understand and predict dynamic processes...
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Shape Parameter Estimation
Performance of machine learning approaches depends strongly on the choic...
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Boosting as a kernelbased method
Boosting combines weak (biased) learners to obtain effective learning al...
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Beating levelset methods for 3D seismic data interpolation: a primaldual alternating approach
Acquisition cost is a crucial bottleneck for seismic workflows, and low...
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Dynamic matrix factorization with social influence
Matrix factorization is a key component of collaborative filteringbased...
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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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Automatic Inference of the Quantile Parameter
Supervised learning is an active research area, with numerous applicatio...
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Robust EM kernelbased methods for linear system identification
Recent developments in system identification have brought attention to r...
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Beyond L2Loss Functions for Learning Sparse Models
Incorporating sparsity priors in learning tasks can give rise to simple,...
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Sparse Quantile Huber Regression for Efficient and Robust Estimation
We consider new formulations and methods for sparse quantile regression ...
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Outlier robust system identification: a Bayesian kernelbased approach
In this paper, we propose an outlierrobust regularized kernelbased met...
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Generalized system identification with stable spline kernels
Regularized leastsquares approaches have been successfully applied to l...
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Accelerating Hessianfree optimization for deep neural networks by implicit preconditioning and sampling
Hessianfree training has become a popular parallel second or der optim...
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Improvements to deep convolutional neural networks for LVCSR
Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Ne...
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Semistochastic Quadratic Bound Methods
Partition functions arise in a variety of settings, including conditiona...
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Fast Dual Variational Inference for NonConjugate LGMs
Latent Gaussian models (LGMs) are widely used in statistics and machine ...
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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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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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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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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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Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation
Recent SVDfree matrix factorization formulations have enabled rank mini...
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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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Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth LogConcave Densities: Modeling, Computation, and Theory
We introduce a class of quadratic support (QS) functions, many of which ...
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Sparse seismic imaging using variable projection
We consider an important class of signal processing problems where the s...
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Smoothing Dynamic Systems with StateDependent Covariance Matrices
Kalman filtering and smoothing algorithms are used in many areas, includ...
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Student's T Robust Bundle Adjustment Algorithm
Bundle adjustment (BA) is the problem of refining a visual reconstructio...
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Aleksandr Y. Aravkin
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WRF Data Science Assistant Professor at University of Washington, Research Staff Member at IBM from 20132015, Adjunct Professor, CS & IEOR at Columbia University in the City of New York from 20142015, Postdoctoral Fellow at The University of British Columbia from 20102012, Research/Teaching Assistant at University of Washington from 20042010, Intern at NASA Ames Research Center 2008