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2019
P. M. Florvaag, V. Naumova and K. Valen-Sendstad. AUTOMATED AND OBJECTIVE SEGMENTATION OF MEDICAL IMAGES USING MACHINE LEARNING TECHNIQUES: ALL MODELS ARE WRONG, BUT SOME ARE USEFUL In Computational and Mathematical Biomedical Engineering. Sendai, Japan: 6th International Conference on Computational & Mathematical Biomedical Engineering (CMBE19), 2019.File 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.
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.
Z. Kereta, T. Klock and V. Naumova. Covariance and precision matrix estimation, and dimension reduction in regression problems In Oslo., 2019.
Z. Kereta, J. Maly and V. Naumova. "Linear convergence and support recovery for non-convex multi-penalty regularization." IEEE TRANSACTIONS ON SIGNAL PROCESSING (2019).
Z. Kereta, V. Naumova and J. Maly. Linear convergence and support recovery for non-convex multi-penalty regularization In SPARS 2019. Toulouse (France),, 2019.
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.
Z. Kereta, T. Klock and V. Naumova. "Nonlinear generalization of the monotone single index model." submitted to Information and Inference (2019).
Z. Kereta, T. Klock and V. Naumova. Towards nonlinear sufficient dimension reduction In AIP conference, Grenoble., 2019.
E. De Vito, Z. Kereta and V. Naumova. "Unsupervised parameter selection for denoising with the elastic net." Machine Learning (2019).
E. De Vito, Z. Kereta and V. Naumova. Unsupervised Parameter Selection in Variational Regularization In SPARS 2019. Toulouse, France, 2019.
2018
M. Grasmair, T. Klock and V. Naumova. "Adaptive multi-penalty regularization based on a generalized Lasso path." Applied and Computational Harmonic Analysis (2018).PDF icon arxiv_paper.pdf (1.86 MB)
M. Fornasier, J. Maly and V. Naumova. "A-T-LAS2,1: A Multi-Penalty Approach to Compressed Sensing of Low-Rank Matrices with Sparse Decompositions." IEEE Transactions on Information Theory (2018).PDF icon 1801.06240-2.pdf (1.53 MB)
V. Naumova and K. Schnass. "Fast Dictionary Learning from Incomplete Data." EURASIP Journal on Advances in Signal Processing 2018, no. 1 (2018): 12.PDF icon 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.
Z. Kereta, T. Klock and V. Naumova. High-dimensional function learning on curves. Cambridge, UK, 2018.
Z. Kereta, T. Klock and V. Naumova. Inference and estimation for nonlinear single index models. Machine Learning Summer School Spain, 2018.
E. De Vito, Z. Kereta and V. Naumova. "A Learning Theory Approach to a Computationally Efficient Parameter Selection for the Elastic Net." arXiv (2018).PDF icon 1809.08696.pdf (1.75 MB)
V. Naumova and Z. Kereta. A machine learning approach for adaptive parameter selection In University of Oslo, Norway., 2018.
V. Naumova and Z. Kereta. A machine learning approach to optimal regularization In European Women in Mathematics, Graz, Austria., 2018.
V. Naumova and Z. Kereta. A machine learning approach to optimal regularization: Affine Manifolds In NTNU, Norway., 2018.PDF icon ntnu.pdf (2.91 MB)
V. Naumova and T. Klock. Multi-parameter regularization for solving inverse problems of unmixing type In University of Cambridge, UK., 2018.PDF icon naumovapresentation1.pdf (2.39 MB)
Z. Kereta, T. Klock and V. Naumova. Nonlinear estimation of single index models In Nonlinear Data: Theory and Algorithms Oberwolfach., 2018.

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