
l1Norm Minimization with Regula Falsi Type Root Finding Methods
Sparse levelset formulations allow practitioners to find the minimum 1...
read it

DataDriven Aerospace Engineering: Reframing the Industry with Machine Learning
Data science, and machine learning in particular, is rapidly transformin...
read it

Efficient Robust Parameter Identification in Generalized Kalman Smoothing Models
Dynamic inference problems in autoregressive (AR/ARMA/ARIMA), exponentia...
read it

IRLS for Sparse Recovery Revisited: Examples of Failure and a Remedy
Compressed sensing is a central topic in signal processing with myriad a...
read it

Trimmed Constrained Mixed Effects Models: Formulations and Algorithms
Mixed effects (ME) models inform a vast array of problems in the physica...
read it

A unified sparse optimization framework to learn parsimonious physicsinformed models from data
Machine learning (ML) is redefining what is possible in dataintensive f...
read it

Sparse Relaxed Regularized Regression: SR3
Regularized regression problems are ubiquitous in statistical modeling, ...
read it

Computer Assisted Localization of a Heart Arrhythmia
We consider the problem of locating a pointsource heart arrhythmia usin...
read it

Generalized Linear Model for Gamma Distributed Variables via Elastic Net Regularization
The Generalized Linear Model (GLM) for the Gamma distribution (glmGamma)...
read it

Sparse Principal Component Analysis via Variable Projection
Sparse principal component analysis (SPCA) has emerged as a powerful tec...
read it

Mean Reverting Portfolios via Penalized OULikelihood Estimation
We study an optimizationbased approach to con struct a meanreverting ...
read it

Fast Robust Methods for Singular StateSpace Models
Statespace models are used in a wide range of time series analysis form...
read it

Learning Robust Representations for Computer Vision
Unsupervised learning techniques in computer vision often require learni...
read it

Time Series Using Exponential Smoothing Cells
Time series analysis is used to understand and predict dynamic processes...
read it

Shape Parameter Estimation
Performance of machine learning approaches depends strongly on the choic...
read it

Boosting as a kernelbased method
Boosting combines weak (biased) learners to obtain effective learning al...
read it

Beating levelset methods for 3D seismic data interpolation: a primaldual alternating approach
Acquisition cost is a crucial bottleneck for seismic workflows, and low...
read it

Dynamic matrix factorization with social influence
Matrix factorization is a key component of collaborative filteringbased...
read it

Dual Smoothing and Level Set Techniques for Variational Matrix Decomposition
We focus on the robust principal component analysis (RPCA) problem, and ...
read it

Automatic Inference of the Quantile Parameter
Supervised learning is an active research area, with numerous applicatio...
read it

Robust EM kernelbased methods for linear system identification
Recent developments in system identification have brought attention to r...
read it

Beyond L2Loss Functions for Learning Sparse Models
Incorporating sparsity priors in learning tasks can give rise to simple,...
read it

Sparse Quantile Huber Regression for Efficient and Robust Estimation
We consider new formulations and methods for sparse quantile regression ...
read it

Outlier robust system identification: a Bayesian kernelbased approach
In this paper, we propose an outlierrobust regularized kernelbased met...
read it

Generalized system identification with stable spline kernels
Regularized leastsquares approaches have been successfully applied to l...
read it

Accelerating Hessianfree optimization for deep neural networks by implicit preconditioning and sampling
Hessianfree training has become a popular parallel second or der optim...
read it

Improvements to deep convolutional neural networks for LVCSR
Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Ne...
read it

Semistochastic Quadratic Bound Methods
Partition functions arise in a variety of settings, including conditiona...
read it

Fast Dual Variational Inference for NonConjugate LGMs
Latent Gaussian models (LGMs) are widely used in statistics and machine ...
read it

Robust and Trend Following Student's t Kalman Smoothers
We present a Kalman smoothing framework based on modeling errors using t...
read it

Linear system identification using stable spline kernels and PLQ penalties
The classical approach to linear system identification is given by param...
read it

Optimization viewpoint on Kalman smoothing, with applications to robust and sparse estimation
In this paper, we present the optimization formulation of the Kalman fil...
read it

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...
read it

Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation
Recent SVDfree matrix factorization formulations have enabled rank mini...
read it

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...
read it

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 ...
read it

Sparse seismic imaging using variable projection
We consider an important class of signal processing problems where the s...
read it

Smoothing Dynamic Systems with StateDependent Covariance Matrices
Kalman filtering and smoothing algorithms are used in many areas, includ...
read it

Student's T Robust Bundle Adjustment Algorithm
Bundle adjustment (BA) is the problem of refining a visual reconstructio...
read it
Aleksandr Y. Aravkin
is this you? claim profile
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