Tag
This paper presents ConsultMind and AutoDisym, frameworks that leverage Bayesian networks and uncertainty-aware reasoning to automate diagnostic consultation, demonstrating improved diagnostic accuracy and explanation quality across medical domains.
This paper proposes a k-order relaxation of the faithfulness assumption for learning graphical Markov blankets, and introduces a proof-of-concept algorithm (kOMB) that can recover Markov blankets even under violations of faithfulness, such as parity-type relationships.
This survey provides a systematic review of federated causal discovery and inference, organizing methods by methodological paradigm, federation topology, and structural scope, and highlighting open challenges.
This paper proposes a Glassbox Framework that uses Bayesian networks as transparent ante-hoc mediation layers for generative models, enabling auditable reasoning traces and contestable outputs to address opacity in high-stakes AI applications.
This paper introduces a parallelization strategy and adaptive steering mechanism for the Baymex algorithm to efficiently learn discretized Bayesian network classifiers for clinical data, achieving speedups over 54x on a 16-core CPU and comparable or better predictive performance than traditional models while maintaining explainability.