Variational Characterization of Free Energy: Theory and Algorithms. - Carsten Hartmann; Lorenz Richter; Christof Schütte; Wei Zhang 065 (Entropy, 2017)

Feedback control theory & Model order reduction for stochastic equations. - Simon Becker; Carsten Hartmann; Martin Redmann; Lorenz Richter (CoRR, 2019)

VarGrad: A Low-Variance Gradient Estimator for Variational Inference. - Lorenz Richter; Ayman Boustati; Nikolas Nüsken; Francisco J. R. Ruiz; Ömer Deniz Akyildiz (NeurIPS, 2020)

Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space. - Nikolas Nüsken; Lorenz Richter (CoRR, 2020)

Model Order Reduction for (Stochastic-) Delay Equations With Error Bounds. - Simon Becker; Lorenz Richter (CoRR, 2020)

Solving high-dimensional parabolic PDEs using the tensor train format. - Lorenz Richter; Leon Sallandt; Nikolas Nüsken (ICML, 2021)

Interpolating between BSDEs and PINNs - deep learning for elliptic and parabolic boundary value problems. - Nikolas Nüsken; Lorenz Richter (CoRR, 2021)

Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning. - Lorenz Richter; Julius Berner (ICML, 2022)

Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness. - Carsten Hartmann; Lorenz Richter (CoRR, 2023)

Early Crop Classification via Multi-Modal Satellite Data Fusion and Temporal Attention. - Frank Weilandt; Robert Behling; Romulo Goncalves; Arash Madadi; Lorenz Richter; Tiago Sanona; Daniel Spengler; Jona Welsch (Remote. Sens., 2023)

Improved sampling via learned diffusions. - Lorenz Richter; Julius Berner (ICLR, 2024)

Fast and unified path gradient estimators for normalizing flows. - Lorenz Vaitl; Ludwig Winkler; Lorenz Richter; Pan Kessel (ICLR, 2024)

Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models. - Ludwig Winkler; Lorenz Richter; Manfred Opper (ICML, 2024)

Dynamical Measure Transport and Neural PDE Solvers for Sampling. - Jingtong Sun; Julius Berner; Lorenz Richter; Marius Zeinhofer; Johannes Müller; Kamyar Azizzadenesheli; Anima Anandkumar (CoRR, 2024)

EuroCropsML: A Time Series Benchmark Dataset For Few-Shot Crop Type Classification. - Joana Reuss; Jan MacDonald; Simon Becker; Lorenz Richter; Marco Körner 001 (CoRR, 2024)

From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs. - Lorenz Richter; Leon Sallandt; Nikolas Nüsken (J. Mach. Learn. Res., 2024)

Nonasymptotic Bounds for Suboptimal Importance Sampling. - Carsten Hartmann; Lorenz Richter (SIAM/ASA J. Uncertain. Quantification, 2024)

Improving Control Based Importance Sampling Strategies for Metastable Diffusions via Adapted Metadynamics. - Enric Ribera Borrell; Jannes Quer; Lorenz Richter; Christof Schütte (SIAM J. Sci. Comput., 2024)

An optimal control perspective on diffusion-based generative modeling. - Julius Berner; Lorenz Richter; Karen Ullrich (Trans. Mach. Learn. Res., 2024)

Underdamped Diffusion Bridges with Applications to Sampling. - Denis Blessing; Julius Berner; Lorenz Richter; Gerhard Neumann (ICLR, 2025)

Sequential Controlled Langevin Diffusions. - Junhua Chen; Lorenz Richter; Julius Berner; Denis Blessing; Gerhard Neumann; Anima Anandkumar (ICLR, 2025)

From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training. - Julius Berner; Lorenz Richter; Marcin Sendera; Jarrid Rector-Brooks; Nikolay Malkin (CoRR, 2025)

Meta-learning For Few-Shot Time Series Crop Type Classification: A Benchmark On The EuroCropsML Dataset. - Joana Reuss; Jan MacDonald; Simon Becker; Konrad Schultka; Lorenz Richter; Marco Körner 001 (CoRR, 2025)

Reinforcement Learning with Random Time Horizons. - Enric Ribera Borrell; Lorenz Richter; Christof Schütte (CoRR, 2025)