s-step self-distillation is optimal among spectral shrinkage estimators for s-spiked covariance matrices and necessary for optimality.
Towards a theory of model distillation.arXiv preprint arXiv:2403.09053, 2024
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InstructMPC uses an LLM plus tunable last layer to map operational context to disturbance trajectories for MPC, proving an O(sqrt(T log T)) regret bound for linear systems and showing lower grid costs on the OpenCEM microgrid.
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Self-Distillation is Optimal Among Spectral Shrinkage Estimators in Spiked Covariance Models
s-step self-distillation is optimal among spectral shrinkage estimators for s-spiked covariance matrices and necessary for optimality.
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Context-Aware Model Predictive Control for Microgrid Energy Management via LLMs
InstructMPC uses an LLM plus tunable last layer to map operational context to disturbance trajectories for MPC, proving an O(sqrt(T log T)) regret bound for linear systems and showing lower grid costs on the OpenCEM microgrid.