repoGitHubTrust 82 · PrimaryPublished 1mo agoLive · 1mo ago
shap/shap
A game theoretic approach to explain the output of any machine learning model.
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 53%Robust Strategic Classification under Decision-Dependent Cost Uncertainty →
- PossiblePossibly related (embedding) · 50%Triadic Werewolf: A Jester Role for Multi-Hop Theory of Mind in LLMs →
- PossiblePossibly related (embedding) · 48%Inference →
- PossiblePossibly related (embedding) · 48%Reliability, Faithfulness, and the Limits of Post-hoc Explanations of Opaque Scientific Models →
- PossiblePossibly related (embedding) · 47%General Intuition’s $2.3B bet that video games can train AI agents for the real world →
- PossiblePossibly related (embedding) · 47%Fun Game - https://numdle-game.pages.dev/ [P] →
- PossiblePossibly related (embedding) · 48%Paradoxes of Game Theoretic Equilibria and Price of Anarchy →
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paperTriadic Werewolf: A Jester Role for Multi-Hop Theory of Mind in LLMspaperRobust Strategic Classification under Decision-Dependent Cost UncertaintynewsGeneral Intuition’s $2.3B bet that video games can train AI agents for the real worldnewsFun Game - https://numdle-game.pages.dev/ [P]paperReliability, Faithfulness, and the Limits of Post-hoc Explanations of Opaque Scientific Modelsglossary_termInferencepaperParadoxes of Game Theoretic Equilibria and Price of Anarchy