Trimpe, Sebastian

37 publications

NeurIPS 2025 BayeSQP: Bayesian Optimization Through Sequential Quadratic Programming Paul Brunzema, Sebastian Trimpe

ICLR 2025 Bayesian Optimization via Continual Variational Last Layer Training Paul Brunzema, Mikkel Jordahn, John Willes, Sebastian Trimpe, Jasper Snoek, James Harrison

ICML 2025 Distributed Event-Based Learning via ADMM Guner Dilsad Er, Sebastian Trimpe, Michael Muehlebach

TMLR 2025 Event-Triggered Time-Varying Bayesian Optimization Paul Brunzema, Alexander von Rohr, Friedrich Solowjow, Sebastian Trimpe

NeurIPS 2025 Kernel Conditional Tests from Learning-Theoretic Bounds Pierre-François Massiani, Christian Fiedler, Lukas Haverbeck, Friedrich Solowjow, Sebastian Trimpe

ICLR 2025 On Rollouts in Model-Based Reinforcement Learning Bernd Frauenknecht, Devdutt Subhasish, Friedrich Solowjow, Sebastian Trimpe

ECML-PKDD 2025 Viability of Future Actions: Robust Safety in Reinforcement Learning via Entropy Regularization Pierre-François Massiani, Alexander von Rohr, Lukas Haverbeck, Sebastian Trimpe

ICMLW 2024 Contextualized Hybrid Ensemble Q-Learning: Learning Fast with Control Priors Emma Cramer, Bernd Frauenknecht, Ramil Sabirov, Sebastian Trimpe

TMLR 2024 Discovering Model Structure of Dynamical Systems with Combinatorial Bayesian Optimization Lucas Rath, Alexander von Rohr, Andreas Schultze, Sebastian Trimpe, Burkhard Corves

L4DC 2024 Event-Triggered Safe Bayesian Optimization on Quadcopters Antonia Holzapfel, Paul Brunzema, Sebastian Trimpe

AAAI 2024 Exact Inference for Continuous-Time Gaussian Process Dynamics Katharina Ensinger, Nicholas Tagliapietra, Sebastian Ziesche, Sebastian Trimpe

AAAI 2024 Learning Hybrid Dynamics Models with Simulator-Informed Latent States Katharina Ensinger, Sebastian Ziesche, Sebastian Trimpe

L4DC 2024 Neural Processes with Event Triggers for Fast Adaptation to Changes Paul Brunzema, Paul Kruse, Sebastian Trimpe

TMLR 2024 On Safety in Safe Bayesian Optimization Christian Fiedler, Johanna Menn, Lukas Kreisköther, Sebastian Trimpe

ICML 2024 On Statistical Learning Theory for Distributional Inputs Christian Fiedler, Pierre-François Massiani, Friedrich Solowjow, Sebastian Trimpe

ICML 2024 On the Consistency of Kernel Methods with Dependent Observations Pierre-François Massiani, Sebastian Trimpe, Friedrich Solowjow

L4DC 2024 Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers Without Retraining Henrik Hose, Alexander Gräfe, Sebastian Trimpe

L4DC 2024 Pointwise-in-Time Diagnostics for Reinforcement Learning During Training and Runtime Noel Brindise, Andres Posada Moreno, Cedric Langbort, Sebastian Trimpe

L4DC 2024 Tracking Object Positions in Reinforcement Learning: A Metric for Keypoint Detection Emma Cramer, Jonas Reiher, Sebastian Trimpe

ICML 2024 Trust the Model Where It Trusts Itself - Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption Bernd Frauenknecht, Artur Eisele, Devdutt Subhasish, Friedrich Solowjow, Sebastian Trimpe

NeurIPSW 2024 Variational Last Layers for Bayesian Optimization Paul Brunzema, Mikkel Jordahn, John Willes, Sebastian Trimpe, Jasper Snoek, James Harrison

AAAI 2023 Combining Slow and Fast: Complementary Filtering for Dynamics Learning Katharina Ensinger, Sebastian Ziesche, Barbara Rakitsch, Michael Tiemann, Sebastian Trimpe

NeurIPS 2023 On Kernel-Based Statistical Learning Theory in the Mean Field Limit Christian Fiedler, Michael Herty, Sebastian Trimpe

TMLR 2023 Recognition Models to Learn Dynamics from Partial Observations with Neural ODEs Mona Buisson-Fenet, Valery Morgenthaler, Sebastian Trimpe, Florent Di Meglio

MLJ 2023 Scale-Preserving Automatic Concept Extraction (SPACE) Andres Felipe Posada-Moreno, Lukas Kreisköther, Tassilo Glander, Sebastian Trimpe

L4DC 2023 Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control Lukas Kesper, Sebastian Trimpe, Dominik Baumann

TMLR 2022 Identifying Causal Structure in Dynamical Systems Dominik Baumann, Friedrich Solowjow, Karl Henrik Johansson, Sebastian Trimpe

ECML-PKDD 2022 Structure-Preserving Gaussian Process Dynamics Katharina Ensinger, Friedrich Solowjow, Sebastian Ziesche, Michael Tiemann, Sebastian Trimpe

NeurIPS 2021 Local Policy Search with Bayesian Optimization Sarah Müller, Alexander von Rohr, Sebastian Trimpe

L4DC 2021 On Exploration Requirements for Learning Safety Constraints Pierre-François Massiani, Steve Heim, Sebastian Trimpe

AAAI 2021 Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression Christian Fiedler, Carsten W. Scherer, Sebastian Trimpe

L4DC 2021 Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes Alexander Rohr, Matthias Neumann-Brosig, Sebastian Trimpe

CoRL 2021 Using Physics Knowledge for Learning Rigid-Body Forward Dynamics with Gaussian Process Force Priors Lucas Rath, Andreas René Geist, Sebastian Trimpe

L4DC 2020 Actively Learning Gaussian Process Dynamics Mona Buisson-Fenet, Friedrich Solowjow, Sebastian Trimpe

L4DC 2020 Learning Constrained Dynamics with Gauss’ Principle Adhering Gaussian Processes Andreas Geist, Sebastian Trimpe

CoRL 2019 A Learnable Safety Measure Steve Heim, Alexander Rohr, Sebastian Trimpe, Alexander Badri-Spröwitz

CoRL 2017 Optimizing Long-Term Predictions for Model-Based Policy Search Andreas Doerr, Christian Daniel, Duy Nguyen-Tuong, Alonso Marco, Stefan Schaal, Marc Toussaint, Sebastian Trimpe