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.
Learning low-dimensional latent dynamics from high-dimensional observations: Non-asymptotics and lower bounds
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Derives instance-specific lower bounds on sample complexity for rank-adaptive matrix estimation and proposes a least-squares plus universal singular-value-thresholding algorithm whose finite-sample error nearly matches those bounds.
citing papers explorer
-
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.
-
Near-optimal Rank Adaptive Inference of High Dimensional Matrices
Derives instance-specific lower bounds on sample complexity for rank-adaptive matrix estimation and proposes a least-squares plus universal singular-value-thresholding algorithm whose finite-sample error nearly matches those bounds.