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.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
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.
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The Fragility of Learning LQG Controllers
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.
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Provably Efficient Sensor Allocation for Unknown High-dimensional Systems with Limited Sensing
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.