10 papers
A Unifying Perspective on Causal World Models: From Observations to Representations to Structure
Avinash Kori, Fabrizio Russo
World Models (WM) are increasingly seen as a foundation for intelligent agents that can predict, plan, and act beyond their training distribution. In this paper, we study WMs from…
Noise-Guided Transport for Imitation Learning
Lionel Blondé, Joao A. Candido Ramos, Alexandros Kalousis
We consider imitation learning in the low-data regime, where only a limited number of expert demonstrations are available. In this setting, methods that rely on large-scale pretrai…
Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regions
Van Khoa Nguyen, Lionel Blondé, Alexandros Kalousis
Training-free diffusion guidance offers a flexible framework for leveraging off-the-shelf classifiers without additional training. Yet, current approaches hinge on posterior approx…
Variational Grey-Box Dynamics Matching
Gurjeet Sangra Singh, Frantzeska Lavda, Giangiacomo Mercatali +1
Deep generative models such as flow matching and diffusion models have shown great potential in learning complex distributions and dynamical systems, but often act as black-boxes,…
Hybrid Generative Modeling for Incomplete Physics: Deep Grey-Box Meets Optimal Transport
Gurjeet Sangra Singh, Maciej Falkiewicz, Alexandros Kalousis
Physics phenomena are often described by ordinary and/or partial differential equations (ODEs/PDEs), and solved analytically or numerically. Unfortunately, many real-world systems…
Simple and Critical Iterative Denoising: A Recasting of Discrete Diffusion in Graph Generation
Yoann Boget
Discrete Diffusion and Flow Matching models have significantly advanced generative modeling for discrete structures, including graphs. However, the dependencies between intermediat…