no code implementations • 12 Mar 2024 • Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Eric Nalisnick
In particular, we leverage conformal prediction to obtain uncertainty intervals with guaranteed coverage for object bounding boxes.
no code implementations • 17 Nov 2023 • Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Eric Nalisnick
We develop a novel multiple hypothesis testing correction with family-wise error rate (FWER) control that efficiently exploits positive dependencies between potentially correlated statistical hypothesis tests.
no code implementations • 30 Aug 2022 • Robert Schmier, Ullrich Köthe, Christoph-Nikolas Straehle
We use a self-supervised feature extractor trained on the auxiliary dataset and train a normalizing flow on the extracted features by maximizing the likelihood on in-distribution data and minimizing the likelihood on the contrastive dataset.
1 code implementation • 3 Mar 2022 • Philipp Geiger, Christoph-Nikolas Straehle
For flexible yet safe imitation learning (IL), we propose theory and a modular method, with a safety layer that enables a closed-form probability density/gradient of the safe generative continuous policy, end-to-end generative adversarial training, and worst-case safety guarantees.
no code implementations • 9 Feb 2022 • Damian Boborzi, Christoph-Nikolas Straehle, Jens S. Buchner, Lars Mikelsons
We propose a non-adversarial learning-from-observations approach, together with an interpretable convergence and performance metric.
no code implementations • 29 Sep 2021 • Damian Boborzi, Christoph-Nikolas Straehle, Jens Stefan Buchner, Lars Mikelsons
Our training objective minimizes the Kulback-Leibler divergence between the policy and expert state transition trajectories which can be optimized in a non-adversarial fashion.
no code implementations • 21 Sep 2020 • Apratim Bhattacharyya, Christoph-Nikolas Straehle, Mario Fritz, Bernt Schiele
This yields an exact inference method that models trajectories at different spatio-temporal resolutions in a hierarchical manner.
2 code implementations • 17 Aug 2020 • Philipp Geiger, Christoph-Nikolas Straehle
For prediction of interacting agents' trajectories, we propose an end-to-end trainable architecture that hybridizes neural nets with game-theoretic reasoning, has interpretable intermediate representations, and transfers to downstream decision making.
no code implementations • 25 May 2020 • Jalal Etesami, Christoph-Nikolas Straehle
This leads to a set of coupled Bellman equations that describes the behavior of the agents.
no code implementations • 24 Aug 2019 • Apratim Bhattacharyya, Michael Hanselmann, Mario Fritz, Bernt Schiele, Christoph-Nikolas Straehle
Prediction of future states of the environment and interacting agents is a key competence required for autonomous agents to operate successfully in the real world.
Ranked #11 on Trajectory Prediction on Stanford Drone