213 citations · 491 across the 20 of their papers we have counts for
21 papers
ParetoTransport: Generative Optimization by Mass Transport Toward The Pareto Front
Stephanie Holly, Sepp Hochreiter, Werner Zellinger
Offline multi-objective optimization requires not only moving the objective vectors of candidate designs toward the Pareto front, but also distributing them effectively along it. G…
Stabilizing In-Context Multi-Source Domain Adaptation for Biomedical Images Through Controls
Ana Sanchez-Fernandez, Thomas Pinetz, Werner Zellinger +1
Biomedical imaging data presents enormous potential for deep learning models to predict invaluable properties, such as diseases and drug effects. However, unavoidable alterations o…
The Offline-Frontier Shift: Diagnosing Distributional Limits in Generative Multi-Objective Optimization
Stephanie Holly, Alexandru-Ciprian Zăvoianu, 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…
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…
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…
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…