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2021
M. Fornasier, T. Klock and K. Riedl. "Consensus-based optimization methods converge globally in mean-field law." arXiv 2103.15130 (2021).
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.PDF icon 1801.06240-2.pdf (1.53 MB)
C. Fiedler, M. Fornasier, T. Klock and M. Rauchensteiner. "Stable Recovery of Entangled Weights: Towards Robust Identification of Deep Neural Networks from Minimal Samples." arXiv (2021).
2020
E. De Vito, M. Fornasier and V. Naumova. "A machine learning approach to optimal Tikhonov regularization I: Affine manifolds." Analysis and Applications (2020).PDF icon learning_parameter.pdf (472.12 KB)
2019
M. Fornasier, E. De Vito, Z. Kereta and V. Naumova. Automated parameter estimation for selected inverse problems In Grenoble, France., 2019.
M. Fornasier, T. Klock and M. Rauchensteiner. Identifying weights of overcomplete shallow- and two layer neural networksusing few network evaluations. Deepmath Conference, New York, USA, 2019.
M. Fornasier, T. Klock and M. Rauchensteiner. "Robust and Resource Efficient Identification of Two Hidden Layer Neural Networks." Constructive Approximation (2019).
2017
M. Fornasier, J. Maly and V. Naumova. Robust Recovery of Low-Rank Matrices using Multi-Penalty Regularization In NIPS Workshop Optimisation for Machine Learning. Long Beach, USA, 2017.
2014
M. Fornasier, V. Naumova and S. V. Pereverzyev. Multi-Parameter Regularization and High-Dimensional Learning. Savannah, USA: SIAM Uncertainty Quantification, 2014.
V. Naumova, M. Fornasier and S. V. Pereverzyev. "Parameter Choice Strategies for Multi-Penalty Regularization." SIAM Journal on Numerical Analysis 52 (2014): 1770-1794.PDF icon simula.simula.2840.pdf (400.4 KB)