Fabian Schaipp

16 papers A* 2B 1Journal 12
YearRankTypeTitle / Venue / Authors
2026 J jnl
Step-Size Stability in Stochastic Optimization: A Theoretical Perspective.
CoRR
Fabian Schaipp, Robert M. Gower, Adrien B. Taylor
2025 J jnl
Analysis of an Idealized Stochastic Polyak Method and its Application to Black-Box Model Distillation.
CoRR
Robert M. Gower, Guillaume Garrigos, Nicolas Loizou, Dimitris Oikonomou, Konstantin Mishchenko, Fabian Schaipp
2025 J jnl
Optimization Benchmark for Diffusion Models on Dynamical Systems.
CoRR
Fabian Schaipp
2025 A* conf
The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training.
ICML
Fabian Schaipp, Alexander Hägele, Adrien B. Taylor, Umut Simsekli, Francis Bach
2025 J jnl
The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training.
CoRR
Fabian Schaipp, Alexander Hägele, Adrien B. Taylor, Umut Simsekli, Francis R. Bach
2025 J jnl
Tracking the Median of Gradients with a Stochastic Proximal Point Method.
Trans. Mach. Learn. Res.
Fabian Schaipp, Guillaume Garrigos, Umut Simsekli, Robert M. Gower
2024 J jnl
A Semismooth Newton Stochastic Proximal Point Algorithm with Variance Reduction.
SIAM J. Optim.
Andre Milzarek, Fabian Schaipp, Michael Ulbrich
2024 A* conf
MoMo: Momentum Models for Adaptive Learning Rates.
ICML
Fabian Schaipp, Ruben Ohana, Michael Eickenberg, Aaron Defazio, Robert M. Gower
2024 J jnl
SGD with Clipping is Secretly Estimating the Median Gradient.
CoRR
Fabian Schaipp, Guillaume Garrigos, Umut Simsekli, Robert M. Gower
2024 — —
Topics in Stochastic Optimization: Learning with Implicit and Adaptive Steps (Implizite und Adaptive Methoden der Stochastischen Optimierung für Machine Learning)
Fabian Schaipp
2023 J jnl
A Stochastic Proximal Polyak Step Size.
CoRR
Fabian Schaipp, Robert M. Gower, Michael Ulbrich
2023 J jnl
A Stochastic Proximal Polyak Step Size.
Trans. Mach. Learn. Res.
Fabian Schaipp, Robert M. Gower, Michael Ulbrich
2023 J jnl
Function Value Learning: Adaptive Learning Rates Based on the Polyak Stepsize and Function Splitting in ERM.
CoRR
Guillaume Garrigos, Robert M. Gower, Fabian Schaipp
2023 J jnl
MoMo: Momentum Models for Adaptive Learning Rates.
CoRR
Fabian Schaipp, Ruben Ohana, Michael Eickenberg, Aaron Defazio, Robert M. Gower
2021 J jnl
GGLasso - a Python package for General Graphical Lasso computation.
J. Open Source Softw.
Fabian Schaipp, Oleg Vlasovets, Christian Müller
2020 B conf
Simulation vs. Testbed: Small Scale Experimental Validation of an Open-Source LTE-A Model.
PIMRC
Lars Wischhof, Fabian Schaipp, Stefan Schuhbäck, Gerta Köster