Aleksandr Y. Aravkin

research

∙ 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...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 05/01/2021

l1-Norm Minimization with Regula Falsi Type Root Finding Methods

Sparse level-set formulations allow practitioners to find the minimum 1-...

0 Metin Vural, et al. ∙

research

∙ 08/24/2020

Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning

Data science, and machine learning in particular, is rapidly transformin...

0 Steven L. Brunton, et al. ∙

research

∙ 10/30/2019

Efficient Robust Parameter Identification in Generalized Kalman Smoothing Models

Dynamic inference problems in autoregressive (AR/ARMA/ARIMA), exponentia...

0 Jonathan Jonker, et al. ∙

research

∙ 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...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 09/24/2019

Trimmed Constrained Mixed Effects Models: Formulations and Algorithms

Mixed effects (ME) models inform a vast array of problems in the physica...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 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...

0 Kathleen Champion, et al. ∙

research

∙ 07/14/2018

Sparse Relaxed Regularized Regression: SR3

Regularized regression problems are ubiquitous in statistical modeling, ...

0 Travis Askham, et al. ∙

research

∙ 07/09/2018

Computer Assisted Localization of a Heart Arrhythmia

We consider the problem of locating a point-source heart arrhythmia usin...

0 Chris Vogl, et al. ∙

research

∙ 04/20/2018

Generalized Linear Model for Gamma Distributed Variables via Elastic Net Regularization

The Generalized Linear Model (GLM) for the Gamma distribution (glmGamma)...

0 Xin Chen, et al. ∙

research

∙ 04/01/2018

Sparse Principal Component Analysis via Variable Projection

Sparse principal component analysis (SPCA) has emerged as a powerful tec...

0 N. Benjamin Erichson, et al. ∙

research

∙ 03/17/2018

Mean Reverting Portfolios via Penalized OU-Likelihood Estimation

We study an optimization-based approach to con- struct a mean-reverting ...

0 Jize Zhang, et al. ∙

research

∙ 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...

0 Jonathan Jonker, et al. ∙

research

∙ 07/31/2017

Learning Robust Representations for Computer Vision

Unsupervised learning techniques in computer vision often require learni...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 06/09/2017

Time Series Using Exponential Smoothing Cells

Time series analysis is used to understand and predict dynamic processes...

0 Avner Abrami, et al. ∙

research

∙ 06/06/2017

Shape Parameter Estimation

Performance of machine learning approaches depends strongly on the choic...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 08/08/2016

Boosting as a kernel-based method

Boosting combines weak (biased) learners to obtain effective learning al...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 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-...

0 Rajiv Kumar, et al. ∙

research

∙ 04/21/2016

Dynamic matrix factorization with social influence

Matrix factorization is a key component of collaborative filtering-based...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 03/01/2016

Dual Smoothing and Level Set Techniques for Variational Matrix Decomposition

We focus on the robust principal component analysis (RPCA) problem, and ...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 11/12/2015

Automatic Inference of the Quantile Parameter

Supervised learning is an active research area, with numerous applicatio...

0 Karthikeyan Natesan Ramamurthy, et al. ∙

research

∙ 11/21/2014

Robust EM kernel-based methods for linear system identification

Recent developments in system identification have brought attention to r...

0 Giulio Bottegal, et al. ∙

research

∙ 03/26/2014

Beyond L2-Loss Functions for Learning Sparse Models

Incorporating sparsity priors in learning tasks can give rise to simple,...

0 Karthikeyan Natesan Ramamurthy, et al. ∙

research

∙ 02/19/2014

Sparse Quantile Huber Regression for Efficient and Robust Estimation

We consider new formulations and methods for sparse quantile regression ...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 12/21/2013

Outlier robust system identification: a Bayesian kernel-based approach

In this paper, we propose an outlier-robust regularized kernel-based met...

0 Giulio Bottegal, et al. ∙

research

∙ 09/30/2013

Generalized system identification with stable spline kernels

Regularized least-squares approaches have been successfully applied to l...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 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...

0 Tara N. Sainath, et al. ∙

research

∙ 09/05/2013

Improvements to deep convolutional neural networks for LVCSR

Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Ne...

0 Tara N. Sainath, et al. ∙

research

∙ 09/05/2013

Semistochastic Quadratic Bound Methods

Partition functions arise in a variety of settings, including conditiona...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 06/05/2013

Fast Dual Variational Inference for Non-Conjugate LGMs

Latent Gaussian models (LGMs) are widely used in statistics and machine ...

0 Mohammad Emtiyaz Khan, et al. ∙

research

∙ 03/22/2013

Robust and Trend Following Student's t Kalman Smoothers

We present a Kalman smoothing framework based on modeling errors using t...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 03/12/2013

Linear system identification using stable spline kernels and PLQ penalties

The classical approach to linear system identification is given by param...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 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...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 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...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 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...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 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...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 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 ...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 12/05/2012

Sparse seismic imaging using variable projection

We consider an important class of signal processing problems where the s...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 11/19/2012

Smoothing Dynamic Systems with State-Dependent Covariance Matrices

Kalman filtering and smoothing algorithms are used in many areas, includ...

0 Aleksandr Y. Aravkin, et al. ∙

research

∙ 11/06/2011

Student's T Robust Bundle Adjustment Algorithm

Bundle adjustment (BA) is the problem of refining a visual reconstructio...

0 Aleksandr Y. Aravkin, et al. ∙

Aleksandr Y. Aravkin

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