Adaptive multi-penalty regularization based on a generalized Lasso path. - Markus Grasmair; Timo Klock; Valeriya Naumova (CoRR, 2017)

Nonlinear generalization of the single index model. - Zeljko Kereta; Timo Klock; Valeriya Naumova (CoRR, 2019)

Robust and Resource Efficient Identification of Two Hidden Layer Neural Networks. - Massimo Fornasier; Timo Klock; Michael Rauchensteiner (CoRR, 2019)

ReLU nets adapt to intrinsic dimensionality beyond the target domain. - Alexander Cloninger; Timo Klock (CoRR, 2020)

Landscape analysis of an improved power method for tensor decomposition. - Joe Kileel 001; Timo Klock; João M. Pereira 002 (NeurIPS, 2021)

Stable Recovery of Entangled Weights: Towards Robust Identification of Deep Neural Networks from Minimal Samples. - Christian Fiedler; Massimo Fornasier; Timo Klock; Michael Rauchensteiner (CoRR, 2021)

Consensus-based optimization methods converge globally in mean-field law. - Massimo Fornasier; Timo Klock; Konstantin Riedl (CoRR, 2021)

A deep network construction that adapts to intrinsic dimensionality beyond the domain. - Alexander Cloninger; Timo Klock (Neural Networks, 2021)

Convergence of Anisotropic Consensus-Based Optimization in Mean-Field Law. - Massimo Fornasier; Timo Klock; Konstantin Riedl (EvoApplications, 2022)

Semi-Supervised Manifold Learning with Complexity Decoupled Chart Autoencoders. - Stefan C. Schonsheck; Scott Mahan; Timo Klock; Alexander Cloninger; Rongjie Lai (CoRR, 2022)

Finite Sample Identification of Wide Shallow Neural Networks with Biases. - Massimo Fornasier; Timo Klock; Marco Mondelli; Michael Rauchensteiner (CoRR, 2022)

Gradient is All You Need? - Konstantin Riedl; Timo Klock; Carina Geldhauser; Massimo Fornasier (CoRR, 2023)

Consensus-Based Optimization Methods Converge Globally. - Massimo Fornasier; Timo Klock; Konstantin Riedl (SIAM J. Optim., 2024)