Derives an ε-local minimax excess-cost lower bound for learning LQG controllers from offline trajectories of a linear exploration policy, expressed via the Hessian of the LQG cost and inverse Fisher information, and instantiates it on fragile robust-control examples.
System identification
5 Pith papers cite this work. Polarity classification is still indexing.
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years
2026 5verdicts
UNVERDICTED 5representative citing papers
Informativity conditions enable unique output prediction for LTI systems from data without uniquely identifying the underlying dynamics.
Drifting MPC produces a unique distribution over trajectories that trades off data support against optimality and enables efficient receding-horizon planning under unknown dynamics.
Develops a two-stage system identification plus sensor allocation algorithm with non-asymptotic guarantees for near-optimal sensor counts in unknown high-dimensional linear systems.
A mixed-integer quadratically constrained optimization learns interpretable stable dynamical models and their Lyapunov functions from data by enforcing stability constraints during training.
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Informativity for Data-driven Prediction
Informativity conditions enable unique output prediction for LTI systems from data without uniquely identifying the underlying dynamics.