Polyak's Heavy Ball Method Achieves Accelerated Local Rate of Convergence under Polyak-Lojasiewicz Inequality,
joint work with Simon Weissmann
(Preprint)
Exponential convergence rates for momentum stochastic gradient descent in the overparametrized setting,
joint work with Benjamin Gess
(Preprint)
Stochastic Modified Flows for Riemannian Stochastic Gradient Descent,
joint work with Benjamin Gess and Nimit Rana
SIAM J. Control Optim. 62(6): 3288-3314
(arXiv,
PDF)
On the existence of minimizers in shallow residual ReLU neural network optimization landscapes,
joint work with Steffen Dereich and Arnulf Jentzen
SIAM J. Numer. Anal. 62(6): 2640-2666 (2024) (arXiv, PDF)
Convergence of Stochastic Gradient Descent Schemes for Lojasiewicz-Landscapes,
joint work with Steffen Dereich
J. Mach. Learn. 3(3): 245-281 (2024) (arXiv, PDF)
On the Existence of Optimal Shallow Feedforward Networks with ReLU Activation,
joint work with Steffen Dereich
J. Mach. Learn. 3(1): 1-22 (2024) (arXiv, PDF)
Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent,
joint work with Benjamin Gess and Vitalii Konarovskyi
J. Mach. Learn. Res. 25(30):1-27 (2024)
(arXiv, PDF)
Central limit theorems for stochastic gradient descent with averaging for stable manifolds,
joint work with Steffen Dereich
Electron. J. Probab. 28, 1-48 (2023) (arXiv, PDF)
Cooling down stochastic differential equations: almost sure convergence,
joint work with Steffen Dereich
Stochastic Process. Appl. 152, 289-311 (2022) (arXiv, PDF)
On minimal representations of shallow ReLU networks,
joint work with Steffen Dereich
Neural Networks 148, 121-128 (2022) (arXiv, PDF)