arXiv:2609.14124v1 Announce Type: cross
Abstract: Medical image analysis is often hindered by biased datasets, which can lead to biased models and limited clinical applicability. A promising strategy...
By Yasin Ibrahim, Robin J. Evans, Konstantinos Kamnitsas
arXiv:2608. 06427v1 Announce Type: new Abstract: Generative models can reproduce an observational distribution while encoding an incorrect causal structure.
By Mojtaba Eslami
arXiv:2603.20111v2 Announce Type: replace
Abstract: The Joint-Embedding Predictive Architecture (JEPA) is often seen as a non-generative alternative to likelihood-based self-supervised learning, emph...
By Moritz G\"ogl, Christopher Yau
arXiv:2609.24879v1 Announce Type: new
Abstract: Counterfactual image generation answers questions about how a subject would have looked under retrospective, hypothetical scenarios. Recent methods hav...
By Xiaodan Xing, Rajat R. Rasal, Julia A. Meister, Sara Ghorayeb, Galvin Khara, Jessica Schrouff
The paper introduces a control‑variable framework for deep neural networks to mitigate omitted variable bias, particularly shortcut learning where covariates like demographics influence predictions. It refits the final layer of a pre‑trained network using cross‑fitting with ridge penalisation, orthogonalises covariate effects, and marginalises predictions over covariate distributions to achieve unbiased, interpretable results. Experiments on simulated images and neuroimaging data show consistent estimation of true effects and performance close to models trained on unconfounded data.
By Manuel Pfeuffer, Roshan Prakash Rane, Kerstin Ritter, Sonja Greven
The paper investigates why joint audio–video generators often learn to predict sound from visual appearance rather than from the underlying event, a problem termed the visual shortcut. By constructing a controlled causal model where audio is independent of video appearance, the authors show that common remedies such as shared latent spaces fail to prevent this shortcut. They propose that intervening on the nuisance appearance is necessary and sufficient for counterfactual invariance, and validate this approach across synthetic and real datasets, highlighting the remaining challenge of unknown nuisances.
By Jian Xu, Delu Zeng, John Paisley