A. M. Bruaset, G. T. Lines and J. Sundnes. "Data aggregation and anonymization for mathematical modeling and epidemiological studies." In Smittestopp - A Case Study on Digital Contact Tracing, edited by A. Elmokashfi, O. Lysne and V. Naumova, 121-141. Vol. 11. Cham: Springer International Publishing, 2022.

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A. Elmokashfi, S. W. Funke, T. Klock, M. Kuchta, V. Naumova and J. J. Uv. "Digital tracing, validation, and reporting." In Smittestopp − A Case Study on Digital Contact Tracing, edited by A. Elmokashfi, O. Lysne and V. Naumova, 99-120. Vol. 11. Cham: Springer International Publishing, 2022.

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A. Elmokashfi, S. W. Funke, T. Klock, M. Kuchta, V. Naumova and J. J. Uv. "Digital tracing, validation, and reporting." In Smittestopp − A Case Study on Digital Contact Tracing, edited by A. Elmokashfi, O. Lysne and V. Naumova, 99-120. Vol. 11. Cham: Springer International Publishing, 2022.

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A. Elmokashfi, O. Lysne and V. Naumova. Smittestopp − A Case Study on Digital Contact Tracing In Simula SpringerBriefs on Computing. Vol. 11. Cham: Springer Nature, 2022.

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Z. Kereta, J. Maly and V. Naumova. "Computational approaches to non-convex, sparsity-inducing multi-penalty regularization." Inverse Problems 37, no. 5 (2021): 055008.

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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 In IEEE Statistical Signal Processing Workshop 2021. IEEE, 2021.

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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 12 (2021).

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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 392 (2021): 125702.

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1801.06240-2.pdf (1.53 MB)

E. De Vito, M. Fornasier and V. Naumova. "A machine learning approach to optimal Tikhonov regularization I: Affine manifolds." Analysis and Applications (2020).

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learning_parameter.pdf (472.12 KB)

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 In Computational and Mathematical Biomedical Engineering. Sendai, Japan: CMBE, 2019.

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no_pdf_yet.docx (21.56 KB)

M. Fornasier, E. De Vito, Z. Kereta and V. Naumova. Automated parameter estimation for selected inverse problems In Grenoble, France., 2019.

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P. M. Florvaag, V. Strøm, C. Glinge, R. Jabbari, N. Vejlstrup, T. Engstrom, K. A. Ahtarovski, T. Jespersen, J. Tfelt-Hansen, V. Naumova et al. A Combined In-Silico and Machine Learning Approach towards Predicting Arrhythmic Risk in Post-Infarction Patients In Computing in Cardiology, Singapore., 2019.

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Z. Kereta, T. Klock and V. Naumova. Covariance and precision matrix estimation, and dimension reduction in regression problems In Oslo, Norway., 2019.

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Z. Kereta, V. Naumova and J. Maly. Linear convergence and support recovery for non-convex multi-penalty regularization. SPARS 2019, Toulouse, France, 2019.

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E. De Vito, Z. Kereta, V. Naumova, L. Rosasco and S. Vigogna. Monte Carlo wavelets: a randomized approach to frame discretization In Sampling Theory and Applications. IEEE, 2019.

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Z. Kereta, T. Klock and V. Naumova. "Nonlinear generalization of the monotone single index model." Information and Inference (2019).

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Z. Kereta, T. Klock and V. Naumova. Towards nonlinear sufficient dimension reduction In AIP conference, Grenoble, France., 2019.

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E. De Vito, Z. Kereta and V. Naumova. "Unsupervised parameter selection for denoising with the elastic net." Machine Learning (2019).

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E. De Vito, Z. Kereta and V. Naumova. Unsupervised Parameter Selection in Variational Regularization. SPARS 2019, Toulouse, France, 2019.

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M. Grasmair, T. Klock and V. Naumova. "Adaptive multi-penalty regularization based on a generalized Lasso path." Applied and Computational Harmonic Analysis (2018).

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arxiv_paper.pdf (1.86 MB)

V. Naumova and K. Schnass. "Fast Dictionary Learning from Incomplete Data." EURASIP Journal on Advances in Signal Processing 2018, no. 1 (2018): 12.

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naumova_et_al-2018-eurasip_journal_on_advances_in_signal_processing.pdf (2.27 MB)

Z. Kereta, T. Klock and V. Naumova. A geometrical approach for nonlinear single-index model estimation. ICML Workshop Stockholm, 2018.

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Z. Kereta, T. Klock and V. Naumova. High-dimensional function learning on curves. Cambridge, UK, 2018.

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Z. Kereta, T. Klock and V. Naumova. Inference and estimation for nonlinear single index models. Machine Learning Summer School Spain, 2018.

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E. De Vito, Z. Kereta and V. Naumova. "A Learning Theory Approach to a Computationally Efficient Parameter Selection for the Elastic Net." arXiv (2018).

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1809.08696.pdf (1.75 MB)