AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work studies the problem of selecting, in an online stream, the agent that best balances predictive performance and subgroup fairness of a sensitive attribute, and that allows for delayed observations of true class labels, as a Bayesian multi-armed bandit problem using Thompson Sampling.
This work provides a general theoretical characterization of the problem of learning to assign prediction tasks to one agent from a set of available agents, including human decision-makers and AI models and develops a framework of sequential explore-exploit policy-learning algorithms that seek to maximize overall perfo...
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