Valeriya Naumova

A. M. Bruaset, G. T. Lines and J. Sundnes

Data aggregation and anonymization for mathematical modeling and epidemiological studies

Smittestopp - A Case Study on Digital Contact Tracing

A. Elmokashfi, S. W. Funke, T. Klock, M. Kuchta, V. Naumova and J. J. Uv

Digital tracing, validation, and reporting

Smittestopp − A Case Study on Digital Contact Tracing

C. Midoglu, B. Ragan-Kelley, S. Reinemo, J. Jahren and P. Halvorsen

Smittestopp Backend

Smittestopp − A Case Study on Digital Contact Tracing

V. Thambawita, S. Hicks, E. Jaouen, P. Halvorsen and M. Riegler

Smittestopp analytics: Analysis of position data

Smittestopp − A Case Study on Digital Contact Tracing

P. M. Florvaag, H. Kjeldsberg and S. K. Mitusch

Smittestopp for Android and iOS

Smittestopp − A Case Study on Digital Contact Tracing

A. Elmokashfi and A. Kvalbein

Using Bluetooth for contact tracing

Smittestopp − A Case Study on Digital Contact Tracing

A. Elmokashfi, O. Lysne and V. Naumova

Smittestopp − A Case Study on Digital Contact Tracing

Simula SpringerBriefs on Computing

Z. Kereta, J. Maly and V. Naumova

Computational approaches to non-convex, sparsity-inducing multi-penalty regularization

Inverse Problems

A. Elmokashfi, J. Sundnes, A. Kvalbein, V. Naumova, S. Reinemo, P. M. Florvaag, H. K. Stensland and O. Lysne

Nationwide rollout reveals efficacy of epidemic control through digital contact tracing

Nature Communications

M. Fornasier, J. Maly and V. Naumova

Robust recovery of low-rank matrices with non-orthogonal sparse decomposition from incomplete measurements

Applied Mathematics and Computation

V. Gogineni, S. R. E. Langberg, V. Naumova, J. F. Nygård, M. Nygård, M. Grasmair and S. Werner

Data-driven Personalized Cervical Cancer Risk Prediction: A Graph-Perspective

IEEE Statistical Signal Processing Workshop 2021

E. De Vito, M. Fornasier and V. Naumova

A machine learning approach to optimal Tikhonov regularization I: Affine manifolds

Analysis and Applications

Z. Kereta, T. Klock and V. Naumova

Nonlinear generalization of the monotone single index model

Information and Inference

E. De Vito, Z. Kereta and V. Naumova

Unsupervised parameter selection for denoising with the elastic net

Machine Learning

P. M. Florvaag, V. Naumova and K. Valen-Sendstad

Automated and objective segmentation of medical image using machine learning techniques: all models are wrong, but some are useful

Computational and Mathematical Biomedical Engineering

E. De Vito, Z. Kereta, V. Naumova, L. Rosasco and S. Vigogna

Monte Carlo wavelets: a randomized approach to frame discretization

Sampling Theory and Applications

Z. Kereta, V. Naumova and J. Maly

Linear convergence and support recovery for non-convex multi-penalty regularization

E. De Vito, Z. Kereta and V. Naumova

Unsupervised Parameter Selection in Variational Regularization

M. Fornasier, E. De Vito, Z. Kereta and V. Naumova

Automated parameter estimation for selected inverse problems

Grenoble, France

Z. Kereta, T. Klock and V. Naumova

Covariance and precision matrix estimation, and dimension reduction in regression problems

Oslo, Norway

Z. Kereta, T. Klock and V. Naumova

Towards nonlinear sufficient dimension reduction

AIP conference, Grenoble, France

P. M. Florvaag, V. Strøm, C. Glinge, R. Jabbari, N. Vejlstrup, T. Engstrom, K. A. Ahtarovski, T. Jespersen, J. Tfelt-Hansen, V. Naumova and H. Arevalo

A Combined In-Silico and Machine Learning Approach towards Predicting Arrhythmic Risk in Post-Infarction Patients

Computing in Cardiology, Singapore

E. De Vito, Z. Kereta and V. Naumova

A Learning Theory Approach to a Computationally Efficient Parameter Selection for the Elastic Net

arXiv

M. Grasmair, T. Klock and V. Naumova

Adaptive multi-penalty regularization based on a generalized Lasso path

Applied and Computational Harmonic Analysis

V. Naumova and K. Schnass

Fast Dictionary Learning from Incomplete Data

EURASIP Journal on Advances in Signal Processing

Z. Kereta, T. Klock and V. Naumova

A geometrical approach for nonlinear single-index model estimation

Z. Kereta, T. Klock and V. Naumova

High-dimensional function learning on curves

Z. Kereta, T. Klock and V. Naumova

Inference and estimation for nonlinear single index models

V. Naumova and Z. Kereta

A machine learning approach for adaptive parameter selection

University of Oslo, Norway

V. Naumova and Z. Kereta

A machine learning approach to optimal regularization

European Women in Mathematics, Graz, Austria

V. Naumova and Z. Kereta

A machine learning approach to optimal regularization: Affine Manifolds

NTNU, Norway

V. Naumova and T. Klock

Multi-parameter regularization for solving inverse problems of unmixing type

University of Cambridge, UK

Z. Kereta, T. Klock and V. Naumova

Nonlinear estimation of single index models

Nonlinear Data: Theory and Algorithms Oberwolfach

M. Grasmair, T. Klock and V. Naumova

Adaptive multi-penalty regularization based on a generalized Lasso path

arXiv

V. Naumova and K. Schnass

Dictionary Learning from Incomplete Data for Efficient Image Restoration

2017 25th European Signal Processing Conference (EUSIPCO)

K. Hlavackova-Schindler, V. Naumova and S. Pereverzyev

Multi-penalty regularization for detecting relevant variables

Recent Applications of Harmonic Analysis to Function Spaces, Differential Equations, and Data Science

M. Fornasier, J. Maly and V. Naumova

Robust Recovery of Low-Rank Matrices using Multi-Penalty Regularization

NIPS Workshop Optimisation for Machine Learning

V. Naumova

Multi-parameter regularisation for solving unmixing problems in signal processing: theoretical and practical aspects

Mathematical Signal Processing and Data Analysis, Bremen, Germany

V. Naumova

A machine learning approach to optimal regularization: affine manifolds

International Workshop Dictionary Learning on Manifolds, Nice, France

V. Naumova

A novel approach for prediction of the future blood glucose evolution in a diabetes patient

AFib-TrainNet Status Seminar, Hamburg, Germany

V. Naumova

Advanced statistics and data analysis for blood glucose prediction and diabetes

Universitäres Herzzentrum Hamburg, Germany

V. Naumova and K. Schnass

Dictionary Learning from Incomplete Data for Efficient Image Restoration

2017 European Signal Processing Conference, Kos Island, Greece

V. Naumova

Image separation using multi-penalty regularization

CEA Saclay, France

V. Naumova

Innovative solution of unmixing problems by means of multi-penalty regularization

Applied Inverse Problems, Hangzhou, China

E. De Vito, Z. Kereta and V. Naumova

Nearly Optimal Parameter selection for the Elastic Net

Hangzhou, China

B. Leon, V. Naumova, E. Ruiz-Velazquez, A. D. McCulloch and E. Sanchez

Combination of Neural Inverse Optimal Control with a Kernel-Based Regularization Learning Algorithm to Prevent Hypoglycemia in Type 1 Diabetes Patients

IEEE Transactions on Neural Networks and Learning Systems

M. Grasmair and V. Naumova

Conditions on optimal support recovery in unmixing problems by means of multi-penalty regularization

Inverse Problems

V. Naumova, L. Nita, J. Poulsen and S. Pereverzyev

Meta-Learning Based Blood Glucose Predictor for Diabetic Smartphone App

Prediction Methods for Blood Glucose Concentration. Lecture Notes in Bioengineering.

V. Naumova

Dictionary Learning from Incomplete Data

2016 SIAM Conference on Imaging Science

V. Naumova

From Big Data to Big Insights

EUMLS Final Conference

V. Naumova

Innovative solution of unmixing problems by means of multi-penalty regularization: theoretical and algorithmical aspects

University of Graz

V. Naumova, K. Hlavackova-Schindler and S. Pereverzyev

Granger Causality for Ill-Posed Problems: Ideas, Methods, and Application in Life Sciences

Statistics and Causality: Methods for Applied Empirical Research

V. Naumova and S. Peter

Minimization of Multi-Penalty Functionals by Alternating Iterative Thresholding and Optimal Parameter Choices

Inverse Problems

K. Hlavackova-Schindler, V. Naumova and S. Pereverzyev

Multi-Penalty Regularization for Detecting Relevant Variables

Computational Statistics and Data Analysis

V. Naumova, M. Fornasier and S. Pereverzyev

Parameter Choice Strategies for Multi-Penalty Regularization

SIAM Journal on Numerical Analysis

V. Naumova, S. Pereverzyev and P. Tkachenko

Regularized collocation for spherical harmonics gravitational field modeling

GEM - International Journal on Geomathematics

V. Naumova, S. Pereverzyev, L. Nita and J. Poulsen

Meta-Learning Based Blood Glucose Predictor for Diabetic Smartphone App

V. Naumova

Autumn School on Mathematical Imaging and Statistical Learning

University of Verona

V. Naumova

Meta-Learning Approach to the Image Denoising Problem

SIAM Conference on Imaging Science

V. Naumova

Meta-Learning Based Blood Glucose Predictor for Diabetic Smartphone App

Workshop on Design, use and evaluation of prediction methods for blood glucose concentration

V. Naumova

Minimization of Multi-Penalty Functionals by Alternating Iterative Thresholding and Optimal Parameter Choices

SIAM Conference on Uncertainty Quantification

V. Naumova

Multi-penalty Regularization for High-Dimensional Data Learning

UCSD

V. Naumova

Numerical Methods for Diabetes Technology

UCSD

M. Fornasier, V. Naumova and S. Pereverzyev

Multi-Parameter Regularization and High-Dimensional Learning

V. Naumova and S. Pereverzyev

Multi-penalty regularization with a component-wise penalization

Inverse Problems

V. Naumova, S. Pereverzyev and S. Sampath

A meta-learning approach to the regularized learning—Case study: Blood glucose prediction

Neural Networks

V. Naumova

Numerical Methods for Diabetes Technology

Mathematical Algorithms for a Better Management of Type 1 Diabetes

S. Sivananthan, V. Naumova, C. D. Man, A. Facchinetti, E. Renard, C. Cobelli and S. Pereverzyev

Assessment of Blood Glucose Predictors: The Prediction-Error Grid Analysis

Diabetes Technology & Therapeutics

V. Naumova, S. Pereverzyev and S. Sampath

Extrapolation in variable RKHSs with application to the blood glucose reading

Inverse Problems

V. Naumova, S. Pereverzyev and S. Sampath

Reading blood glucose from subcutaneous electric current by means of a regularization in variable Reproducing Kernel Hilbert Spaces

IEEE Conference on Decision and Control and European Control Conference