seminars
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Robust and Actionable ML via Causality
Abstract: Artificial Intelligence is increasingly deployed in high-stakes fields such as healthcare, where two critical challenges emerge: models must generalize to real-world variations and their predictions must be actionable for decision-makers. The generalization challenge arises when AI is applied to data that differs from its training set, a phenomenon known as distribution shift. The actionability challenge… Continue Reading Robust and Actionable ML via Causality
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Robust and Actionable ML via Causality
Abstract: Artificial Intelligence is increasingly deployed in high-stakes fields such as healthcare, where two critical challenges emerge: models must generalize to real-world variations and their predictions must be actionable for decision-makers. The generalization challenge arises when AI is applied to data that differs from its training set, a phenomenon known as distribution shift. The actionability challenge… Continue Reading Robust and Actionable ML via Causality
