AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Causal discovery from observational data is a fundamental yet challenging task in scientific research. While existing approaches are primarily based on conditional independence tests, structure scores, or restrictive functional assumptions, we propose Decoupled Causal Discovery (DCD), a novel decoupling-based perspecti...
StablePFN is proposed, a novel tabular foundation model that integrates explicit causal awareness with stable predictive modeling and significantly outperforms state-of-the-art baselines in cross-environment prediction settings, particularly in challenging high-bias scenarios.
Pre-trained tabular prediction models based on Prior-Data Fitted Networks (PFNs), such as TabPFN and LimiX, have achieved remarkable progress in supervised learning, demonstrating immense potential across real-world scenarios and diverse downstream tasks. However, a critical question remains systematically unexplored:...
DAG-FM is proposed, a novel foundation model architecture that amortizes causal discovery and introduces Mixture-of-Leaf-Experts (MoLE) to handle diverse and unknown Functional Causal Model (FCM) assumptions in real-world scenarios.
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