4 papers
The Offline-Frontier Shift: Diagnosing Distributional Limits in Generative Multi-Objective Optimization
Stephanie Holly, Alexandru-Ciprian ZÄvoianu, Siegfried Silber +2
Offline multi-objective optimization (MOO) aims to recover Pareto-optimal designs given a finite, static dataset. Recent generative approaches, including diffusion models, show str…
AP-OOD: Attention Pooling for Out-of-Distribution Detection
Claus Hofmann, Christian Huber, Bernhard Lehner +3
Out-of-distribution (OOD) detection, which maps high-dimensional data into a scalar OOD score, is critical for the reliable deployment of machine learning models. A key challenge i…
Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned
Kajetan Schweighofer, Barbara Brune, Lukas Gruber +11
There is an increasing adoption of artificial intelligence in safety-critical applications, yet practical schemes for certifying that AI systems are safe, lawful and socially accep…
Binary Losses for Density Ratio Estimation
Werner Zellinger
Estimating the ratio of two probability densities from a finite number of observations is a central machine learning problem. A common approach is to construct estimators using bin…