Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes. - Steffen Dereich; Sebastian Kassing (CoRR, 2021)

Stochastic approximation with a focus on machine learning applications. - Sebastian Kassing (2021)

On minimal representations of shallow ReLU networks. - Steffen Dereich; Sebastian Kassing (Neural Networks, 2022)

Convergence rates for momentum stochastic gradient descent with noise of machine learning type. - Benjamin Gess; Sebastian Kassing (CoRR, 2023)

On the existence of optimal shallow feedforward networks with ReLU activation. - Steffen Dereich; Sebastian Kassing (CoRR, 2023)

Polyak's Heavy Ball Method Achieves Accelerated Local Rate of Convergence under Polyak-Lojasiewicz Inequality. - Sebastian Kassing; Simon Weissmann (CoRR, 2024)

Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent. - Benjamin Gess; Sebastian Kassing; Vitalii Konarovskyi (J. Mach. Learn. Res., 2024)

Stochastic Modified Flows for Riemannian Stochastic Gradient Descent. - Benjamin Gess; Sebastian Kassing; Nimit Rana (SIAM J. Control. Optim., 2024)

On the Existence of Minimizers in Shallow Residual ReLU Neural Network Optimization Landscapes. - Steffen Dereich; Arnulf Jentzen; Sebastian Kassing (SIAM J. Numer. Anal., 2024)