OperatorSHAP trains FastSHAP-style explainers for neural operators via a function-space attribution framework that remains consistent across grid resolutions without retraining.
Fastshap: Real-time shapley value estimation
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DUET is a global-to-local method that optimizes LLM training data mixtures via Bayesian optimization guided by influence-based selection and feedback from unseen evaluation tasks, with a regret bound showing convergence to the optimal mixture.
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OperatorSHAP: Fast and Accurate Shapley Value Estimation for Neural Operators
OperatorSHAP trains FastSHAP-style explainers for neural operators via a function-space attribution framework that remains consistent across grid resolutions without retraining.
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DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks
DUET is a global-to-local method that optimizes LLM training data mixtures via Bayesian optimization guided by influence-based selection and feedback from unseen evaluation tasks, with a regret bound showing convergence to the optimal mixture.