21 citations · 27 across the 30 of their papers we have counts for
7 papers · 1 filter
Clustered Randomized Smoothing for Stochastic Prediction Functions
Eduardo Figueiredo, Frederik Mathiesen, Julian Schumann +3
Modern stochastic predictors can model rich, multi-modal outcome distributions. However, this expressive power comes with challenges in ensuring robust predictions a critical r…
Evaluating randomized smoothing as a defense against adversarial attacks in trajectory prediction
Julian F. Schumann, Eduardo Figueiredo, Frederik Baymler Mathiesen +3
Accurate and robust trajectory prediction is essential for safe and efficient autonomous driving, yet recent work has shown that even state-of-the-art prediction models are highly…
Search Inspired Exploration in Reinforcement Learning
Georgios Sotirchos, Zlatan Ajanović, Jens Kober
Exploration in environments with sparse rewards remains a fundamental challenge in reinforcement learning (RL). Existing approaches such as curriculum learning and Go-Explore often…
STEP: Structured Training and Evaluation Platform for benchmarking trajectory prediction models
Julian F. Schumann, Anna Mészáros, Jens Kober +1
While trajectory prediction plays a critical role in enabling safe and effective path-planning in automated vehicles, standardized practices for evaluating such models remain under…
ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning
Jelle Luijkx, Zlatan Ajanović, Laura Ferranti +1
Human teaching effort is a significant bottleneck for the broader applicability of interactive imitation learning. To reduce the number of required queries, existing methods employ…
ROME: Robust Multi-Modal Density Estimator
Anna Mészáros, Julian F. Schumann, Javier Alonso-Mora +2
The estimation of probability density functions is a fundamental problem in science and engineering. However, common methods such as kernel density estimation (KDE) have been demon…
Predictable Reinforcement Learning Dynamics through Entropy Rate Minimization
Daniel Jarne Ornia, Giannis Delimpaltadakis, Jens Kober +1
In Reinforcement Learning (RL), agents have no incentive to exhibit predictable behaviors, and are often pushed (through e.g. policy entropy regularisation) to randomise their acti…