AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes a framework for computing CXs for inputs with missing values using flow-based generative models and Mixed-Integer Linear Programming (MILP) and demonstrates that this method is able to generate CXs with higher validity, and lower cost than state-of-the-art methods relying on imputation.
Three lines of work are considered that build on standard counterfactual explanations and ask what would need to change for them to be better aligned with deployment requirements, including one that extends them beyond one-shot decisions to capture sequential decision-making.
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