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Metric Elicitation and Robust Distributed Learning with Sanmi Koyejo - #352

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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Understanding Generalizability in Machine Learning

This chapter explores the complexities of achieving generalizability in machine learning with a focus on binary classification. It discusses the significance of selecting appropriate data and how error tradeoffs in confusion matrices can impact model performance, particularly in relation to human preferences and fairness metrics. The conversation also highlights ongoing research efforts aimed at enhancing metric complexity for better alignment with expert insights and practical usability.

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