59 citations · 71 across the 8 of their papers we have counts for
9 papers
Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction
Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann +1
Quantifying a model's predictive uncertainty is essential for safety-critical applications such as autonomous driving. We consider quantifying such uncertainty for multi-object det…
Max-Rank: Efficient Multiple Testing for Conformal Prediction
Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann +2
Multiple hypothesis testing (MHT) frequently arises in scientific inquiries, and concurrent testing of multiple hypotheses inflates the risk of Type-I errors or false positives, re…
Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data
Robert Schmier, Ullrich Köthe, Christoph-Nikolas Straehle
Detecting test data deviating from training data is a central problem for safe and robust machine learning. Likelihoods learned by a generative model, e.g., a normalizing flow via…
Fail-Safe Adversarial Generative Imitation Learning
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 gener…
Imitation Learning by State-Only Distribution Matching
Damian Boborzi, Christoph-Nikolas Straehle, Jens S. Buchner +1
Imitation Learning from observation describes policy learning in a similar way to human learning. An agent's policy is trained by observing an expert performing a task. While many…
Haar Wavelet based Block Autoregressive Flows for Trajectories
Apratim Bhattacharyya, Christoph-Nikolas Straehle, Mario Fritz +1
Prediction of trajectories such as that of pedestrians is crucial to the performance of autonomous agents. While previous works have leveraged conditional generative models like GA…