5 papers · 1 filter
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…
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…
SymbolicAI: A framework for logic-based approaches combining generative models and solvers
Marius-Constantin Dinu, Claudiu Leoveanu-Condrei, Markus Holzleitner +2
We introduce SymbolicAI, a versatile and modular framework employing a logic-based approach to concept learning and flow management in generative processes. SymbolicAI enables the…
Overcoming Saturation in Density Ratio Estimation by Iterated Regularization
Lukas Gruber, Markus Holzleitner, Johannes Lehner +2
Estimating the ratio of two probability densities from finitely many samples, is a central task in machine learning and statistics. In this work, we show that a large class of kern…