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DTSTART;TZID=America/Los_Angeles:20251027T161500
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CREATED:20251006T191634Z
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UID:3885-1761581700-1761585300@colleges.claremont.edu
SUMMARY:Estimating Shapley Values for Explainable AI via Richer Model Approximations (Teal Witter\, CMC)
DESCRIPTION:Abstract: Modern machine learning is ultimately a simple process: We iteratively update the weights of machine learning models to minimize a problem-specific loss. When it works well\, we deploy the model in human-facing domains like healthcare\, finance\, or the justice system. But even though we know how models are trained\, we don’t understand why they make decisions the decision they do. A particularly compelling approach to explaining AI predictions is the Shapley value\, a game-theoretic quantity that measures how each input to the model affects its output. Mathematically\, the ith Shapley value is the average change in the ith dimension of a particular function defined on the d-dimensional hypercube. Because the hypercube has 2^d points\, exactly computing Shapley values is infeasible. In this talk\, we will instead leverage algorithmic insights to develop state-of-the-art approximation methods.
URL:https://colleges.claremont.edu/ccms/event/estimating-shapley-values-for-explainable-ai-via-richer-model-approximations-teal-witter-cmc/
LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States
CATEGORIES:Applied Math Seminar
ORGANIZER;CN="Ryan Aschoff":MAILTO:ryan.aschoff@cgu.edu
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