Simon Weissmann

According to our database, Simon Weissmann authored at least 29 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Error Bounds for Importance Sampling with Estimated Proposal Distributions.

Cathrine Aeckerle-Willems, Ilja Klebanov, Simon Weissmann

CoRR, May, 2026

Derivative-free stochastic bilevel optimization for inverse problems.

Mathias Staudigl, Simon Weissmann, Tristan van Leeuwen

Comput. Optim. Appl., April, 2026

Ensemble Kalman inversion with non-smooth regularization.

Simon Weissmann

CoRR, March, 2026

A Stochastic Gradient Descent Approach to Design Policy Gradient Methods for LQR.

Bowen Song, Simon Weissmann, Mathias Staudigl, Andrea Iannelli

CoRR, February, 2026

The Role of Target Update Frequencies in Q-Learning.

Simon Weissmann, Tilman Aach, Benedikt Wille, Sebastian Kassing, Leif Döring

CoRR, February, 2026

An Approximate Ascent Approach To Prove Convergence of PPO.

Leif Döring, Daniel Schmidt, Moritz Melcher, Sebastian Kassing, Benedikt Wille, Tilman Aach, Simon Weissmann

CoRR, February, 2026

2025

Adaptive Kernel Selection for Stein Variational Gradient Descent.

Moritz Melcher, Simon Weissmann, Ashia C. Wilson, Jakob Zech

CoRR, October, 2025

Metropolis-adjusted interacting particle sampling.

Björn Sprungk, Simon Weissmann, Jakob Zech

Stat. Comput., June, 2025

Clustered KL-barycenter design for policy evaluation.

Simon Weissmann, Till Freihaut, Claire Vernade, Giorgia Ramponi, Leif Döring

CoRR, March, 2025

Almost Sure Convergence of Stochastic Gradient Methods under Gradient Domination.

Simon Weissmann, Sara Klein, Waïss Azizian, Leif Döring

Trans. Mach. Learn. Res., 2025

Controlling the Flow: Stability and Convergence for Stochastic Gradient Descent with Decaying Regularization.

Sebastian Kassing, Simon Weissmann, Leif Döring

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

2024

One-Shot Learning of Surrogates in PDE-Constrained Optimization under Uncertainty.

Philipp A. Guth, Claudia Schillings, Simon Weissmann

SIAM/ASA J. Uncertain. Quantification, 2024

Adaptive Multilevel Subset Simulation with Selective Refinement.

Daniel Elfverson, Robert Scheichl, Simon Weissmann, Francisco Alejandro Diaz De la O

SIAM/ASA J. Uncertain. Quantification, 2024

Structure Matters: Dynamic Policy Gradient.

Sara Klein, Xiangyuan Zhang, Tamer Basar, Simon Weissmann, Leif Döring

CoRR, 2024

Polyak's Heavy Ball Method Achieves Accelerated Local Rate of Convergence under Polyak-Lojasiewicz Inequality.

Sebastian Kassing, Simon Weissmann

CoRR, 2024

Almost sure convergence rates of stochastic gradient methods under gradient domination.

Simon Weissmann, Sara Klein, Waïss Azizian, Leif Döring

CoRR, 2024

On the mean-field limit for Stein variational gradient descent: stability and multilevel approximation.

Simon Weissmann, Jakob Zech

CoRR, 2024

The Ensemble Kalman Filter for Dynamic Inverse Problems.

Simon Weissmann, Neil K. Chada, Xin T. Tong

CoRR, 2024

Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods.

Sara Klein, Simon Weissmann, Leif Döring

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

On the ensemble Kalman inversion under inequality constraints.

Matei Hanu, Simon Weissmann

CoRR, 2023

2022

Continuous Time Limit of the Stochastic Ensemble Kalman Inversion: Strong Convergence Analysis.

Dirk Blömker, Claudia Schillings, Philipp Wacker, Simon Weissmann

SIAM J. Numer. Anal., December, 2022

Gradient flow structure and convergence analysis of the ensemble Kalman inversion for nonlinear forward models.

Simon Weissmann

CoRR, 2022

Multilevel Optimization for Inverse Problems.

Simon Weissmann, Ashia Wilson, Jakob Zech

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Fokker-Planck Particle Systems for Bayesian Inference: Computational Approaches.

Sebastian Reich, Simon Weissmann

SIAM/ASA J. Uncertain. Quantification, 2021

A General Framework for Machine Learning based Optimization Under Uncertainty.

Philipp A. Guth, Claudia Schillings, Simon Weissmann

CoRR, 2021

Adaptive Tikhonov strategies for stochastic ensemble Kalman inversion.

Simon Weissmann, Neil K. Chada, Claudia Schillings, Xin T. Tong

CoRR, 2021

2020

Consistency analysis of bilevel data-driven learning in inverse problems.

Neil K. Chada, Claudia Schillings, Xin T. Tong, Simon Weissmann

CoRR, 2020

Ensemble Kalman filter for neural network based one-shot inversion.

Philipp A. Guth, Claudia Schillings, Simon Weissmann

CoRR, 2020

2019

On the Incorporation of Box-Constraints for Ensemble Kalman Inversion.

Neil K. Chada, Claudia Schillings, Simon Weissmann

CoRR, 2019