59 citations · 71 across the 8 of their papers we have counts for
3 papers · 1 filter
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…