Simulation vs. Testbed: Small Scale Experimental Validation of an Open-Source LTE-A Model. - Lars Wischhof; Fabian Schaipp; Stefan Schuhbäck; Gerta Köster (PIMRC, 2020)

GGLasso - a Python package for General Graphical Lasso computation. - Fabian Schaipp; Oleg Vlasovets; Christian Müller (J. Open Source Softw., 2021)

Function Value Learning: Adaptive Learning Rates Based on the Polyak Stepsize and Function Splitting in ERM. - Guillaume Garrigos; Robert M. Gower; Fabian Schaipp (CoRR, 2023)

A Stochastic Proximal Polyak Step Size. - Fabian Schaipp; Robert M. Gower; Michael Ulbrich (Trans. Mach. Learn. Res., 2023)

MoMo: Momentum Models for Adaptive Learning Rates. - Fabian Schaipp; Ruben Ohana; Michael Eickenberg; Aaron Defazio; Robert M. Gower (ICML, 2024)

SGD with Clipping is Secretly Estimating the Median Gradient. - Fabian Schaipp; Guillaume Garrigos; Umut Simsekli; Robert M. Gower (CoRR, 2024)

Topics in Stochastic Optimization: Learning with Implicit and Adaptive Steps (Implizite und Adaptive Methoden der Stochastischen Optimierung für Machine Learning) - Fabian Schaipp (2024)

A Semismooth Newton Stochastic Proximal Point Algorithm with Variance Reduction. - Andre Milzarek; Fabian Schaipp; Michael Ulbrich (SIAM J. Optim., 2024)

The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training. - Fabian Schaipp; Alexander Hägele; Adrien B. Taylor; Umut Simsekli; Francis R. Bach (CoRR, 2025)

Analysis of an Idealized Stochastic Polyak Method and its Application to Black-Box Model Distillation. - Robert M. Gower; Guillaume Garrigos; Nicolas Loizou; Dimitris Oikonomou; Konstantin Mishchenko; Fabian Schaipp (CoRR, 2025)