Publikation
Credibility-Aware Multimodal Fusion Using Probabilistic Circuits
Sahil Sidheekh; Pranuthi Tenali; Saurabh Mathur; Erik Blasch; Kristian Kersting; Sriraam Natarajan
In: Yingzhen Li; Stephan Mandt; Shipra Agrawal; Mohammad Sadil Khan (Hrsg.). International Conference on Artificial Intelligence and Statistics, AISTATS 2025, Mai Khao, Thailand, 3-5 May 2025. International Conference on Artificial Intelligence and Statistics (AISTATS), Pages 2305-2313, Proceedings of Machine Learning Research, Vol. 258, PMLR, 2025.
Zusammenfassung
We consider the problem of late multi-modal fu-
sion for discriminative learning. Motivated by
noisy, multi-source domains that require under-
standing the reliability of each data source, we
explore the notion of credibility in the context of
multi-modal fusion. We propose a combination
function that uses probabilistic circuits (PCs) to
combine predictive distributions over individual
modalities. We also define a probabilistic measure
to evaluate the credibility of each modality via
inference queries over the PC. Our experimental
evaluation demonstrates that our fusion method can
reliably infer credibility while maintaining com-
petitive performance with the state-of-the-art.
