6 papers
Closing the Distribution Gap in Adversarial Training for LLMs
Chengzhi Hu, Jonas Dornbusch, David Lüdke +2
Adversarial training for LLMs is one of the most promising methods to reliably improve robustness against adversaries. However, despite significant progress, models remain vulnerab…
Edit-Based Flow Matching for Temporal Point Processes
David Lüdke, Marten Lienen, Marcel Kollovieh +1
Temporal point processes (TPPs) are a fundamental tool for modeling event sequences in continuous time, but most existing approaches rely on autoregressive parameterizations that a…
Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling
Niclas Dern, Lennart Redl, Sebastian Pfister +3
Sampling from unnormalized target distributions, e.g.\ Boltzmann distributions , is fundamental to many scientific applications yet comp…
Unlocking Point Processes through Point Set Diffusion
David Lüdke, Enric Rabasseda Raventós, Marcel Kollovieh +1
Point processes model the distribution of random point sets in mathematical spaces, such as spatial and temporal domains, with applications in fields like seismology, neuroscience,…
Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting
Marcel Kollovieh, Marten Lienen, David Lüdke +2
Recent advancements in generative modeling, particularly diffusion models, have opened new directions for time series modeling, achieving state-of-the-art performance in forecastin…
Landmark-free Statistical Shape Modeling via Neural Flow Deformations
David Lüdke, Tamaz Amiranashvili, Felix Ambellan +3
Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population. Shape models are employed in many tasks, such as shap…