Author

J. Boedecker

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#artificial intelligence Preprint Sep 2026

Comfort by Construction: Adaptive, Comfort-Bounded Action Spaces for Learned Driving Policies

Data-driven driving simulators command accelerations and steering rates from a fixed grid without constraining the realized accelerations and jerks. As a result, reinforcement-learning policies inflate safety metrics through abrupt, last-second maneuvers that lie far outside the range of human driving and would be unac...

Anna Rothenhäusler, Daniel Jost, Raghu Rajan et al. · 0 citations

Scalable Perturbation-Based Explanations via Tradeoff-Conditioned Smooth Masking

This work introduces a sampling-free, perturbation-based training framework based on continuous and differentiable masking that achieves competitive or superior attribution faithfulness compared to strong sampling-based baselines, while dramatically reducing computational cost and enabling substantially improved scalab...

Mehdi Naouar, Jens Rahnfeld, Yannick Vogt et al. · 0 citations

#machine learning Preprint Sep 2026

Disciplined Bilevel Programming

DBLP is introduced, a symbolic framework that allows users to specify and solve optimistic bilevel problems in a high-level, human-readable way that is close to the mathematical formulation.

Hao Zhu, J. Boedecker · 0 citations