AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The workshop brings together researchers and practitioners from data mining, LLMs, NLP, NLP, IR, human-centered AI, and AI safety to position personalization as a central research direction for next-generation AI systems at KDD.
This work proposes a model-agnostic framework to assess the accuracy of imputed labels and to correct imputations with large bias based on conformal prediction, and leverage conformal prediction to construct a valid prediction set for units with unobserved labels, and revise imputations that fall outside this set.
Large language models (LLMs) and agentic AI systems are rapidly moving into user-facing applications, yet most remain fundamentally generic, optimized for population-level objectives under the assumption that one model can serve all users. This assumption is increasingly misaligned with real-world deployment, where AI...
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