Game Theory for Model Interpretability: Shapley Values

Game Theory for Model Interpretability: Shapley Values

As machine learning models get into the hands of more and more users, there's an increasing expectation that black box isn't good enough: users want to understand why the model made a given prediction, not just what the prediction itself is. This is motivating a lot of work into feature important and model interpretability tools, and one of the most exciting new ones is based on Shapley Values from game theory. In this episode, we'll explain what Shapley Values are and how they make a cool approach to feature importance for machine learning.

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Episoder(321)

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Reasoning Models: When LLMs Went Beyond Fancy Autocomplete

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Distillation, or, How to Steal a Model

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27 Jul 23min

Invisible LLM Failures and AI Fluency with Chris Potts (Stanford)

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20 Jul 41min

Still summer break: back next week

Still summer break: back next week

Still summer break: back next week by Katie Malone

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Summer break: back soon

Summer break: back soon

Summer break: back soon by Katie Malone

6 Jul 36s

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