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2021
Z. Kereta, J. Maly and V. Naumova. "Computational approaches to non-convex, sparsity-inducing multi-penalty regularization." Inverse Problems 37, no. 5 (2021): 055008.
Z. Kereta and T. Klock. "Estimating covariance and precision matrices along subspaces." Electronic Journal of Statistics 15, no. 1 (2021): 554-588.
2019
M. Fornasier, E. De Vito, Z. Kereta and V. Naumova. Automated parameter estimation for selected inverse problems In Grenoble, France., 2019.
Z. Kereta, T. Klock and V. Naumova. Covariance and precision matrix estimation, and dimension reduction in regression problems In Oslo, Norway., 2019.
Z. Kereta, V. Naumova and J. Maly. Linear convergence and support recovery for non-convex multi-penalty regularization. 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." Information and Inference (2019).
Z. Kereta, T. Klock and V. Naumova. Towards nonlinear sufficient dimension reduction In AIP conference, Grenoble, France., 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. SPARS 2019, Toulouse, France, 2019.
2018
Z. Kereta and P. Grohs. "Analysis of edge and corner points using parabolic dictionaries." Applied and Computational Harmonic Analysis (2018).
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)
Z. Kereta, T. Klock and V. Naumova. Nonlinear estimation of single index models In Nonlinear Data: Theory and Algorithms Oberwolfach., 2018.
2017
E. De Vito, Z. Kereta and V. Naumova. Nearly Optimal Parameter selection for the Elastic Net In Hangzhou, China., 2017.