Sebastian Trimpe
27 papers · 2015–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π Interdisciplinary Bridge π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (10) π Conference Polyglot (7)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Interdisciplinary Bridge
π
Grand Slam
ποΈ
Keyword Collector
(103)
π
Conference Pioneer
π
Trend Setter
π
Century Club
(27)
β‘
Prolific Year
(5)
Conferences
L4DC (10)
AAAI (4)
ICML (4)
CORL (3)
ICLR (2)
NIPS (2)
RSS (2)
Top co-authors
Keywords
dynamical system
(6)
gaussian process
(6)
reinforcement learning
(3)
bayesian optimization
(3)
recurrent neural network
(2)
active learning
(2)
learning-based control
(2)
uncertainty quantification
(2)
long-term prediction
(2)
active sampling
(2)
policy search
(2)
gaussian process regression
(2)
event-triggered control
(2)
communication policies
(1)
automatic differentiation
(1)
statistical learning theory
(1)
model predictive control
(1)
object tracking
(1)
bayesian inference
(1)
explainable ai
(1)
Papers
Bayesian Optimization via Continual Variational Last Layer Training
ICLR 2025
Distributed Event-Based Learning via ADMM
ICML 2025
On Rollouts in Model-Based Reinforcement Learning
ICLR 2025
Neural processes with event triggers for fast adaptation to changes
L4DC 2024
Exact Inference for Continuous-Time Gaussian Process Dynamics
AAAI 2024
Learning Hybrid Dynamics Models with Simulator-Informed Latent States
AAAI 2024
Pointwise-in-time diagnostics for reinforcement learning during training and runtime
L4DC 2024
Event-triggered safe Bayesian optimization on quadcopters
L4DC 2024
Tracking object positions in reinforcement learning: A metric for keypoint detection
L4DC 2024
Trust the Model Where It Trusts Itself - Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption
ICML 2024
On the Consistency of Kernel Methods with Dependent Observations
ICML 2024
Parameter-adaptive approximate MPC: Tuning neural-network controllers without retraining
L4DC 2024
On Statistical Learning Theory for Distributional Inputs
ICML 2024
Combining Slow and Fast: Complementary Filtering for Dynamics Learning
AAAI 2023
Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control
L4DC 2023
On kernel-based statistical learning theory in the mean field limit
NIPS 2023
Local policy search with Bayesian optimization
NIPS 2021
Using Physics Knowledge for Learning Rigid-body Forward Dynamics with Gaussian Process Force Priors
CORL 2021
Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression
AAAI 2021
Probabilistic robust linear quadratic regulators with Gaussian processes
L4DC 2021
On exploration requirements for learning safety constraints
L4DC 2021
Learning Constrained Dynamics with Gaussβ Principle adhering Gaussian Processes
L4DC 2020
Actively Learning Gaussian Process Dynamics
L4DC 2020
Learning of Sub-optimal Gait Controllers for Magnetic Walking Soft Millirobots
RSS 2020
A Learnable Safety Measure
CORL 2019
Optimizing Long-term Predictions for Model-based Policy Search
CORL 2017
A New Perspective and Extension of the Gaussian Filter
RSS 2015