Search Results for author:
Found 7 papers, 0 papers with code
Finite Sample Identification of Bilinear Dynamical Systems
This motivates the problem of learning bilinear systems from a single trajectory of the system's states and inputs.
Identification and Adaptive Control of Markov Jump Systems: Sample Complexity and Regret Bounds
Combining our sample complexity results with recent perturbation results for certainty equivalent control, we prove that when the episode lengths are appropriately chosen, the proposed adaptive control scheme achieves $\mathcal{O}(\sqrt{T})$ regret, which can be improved to $\mathcal{O}(polylog(T))$ with partial knowledge of the system.
Certainty Equivalent Quadratic Control for Markov Jump Systems
Real-world control applications often involve complex dynamics subject to abrupt changes or variations.
Estimation of Groundwater Storage Variations in Indus River Basin using GRACE Data
We also present the design of a band-limited optimally concentrated window function for spatial localization of the data in the region of interest.
Exploring Weight Importance and Hessian Bias in Model Pruning
Model pruning is an essential procedure for building compact and computationally-efficient machine learning models.
Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems
If the system is run by a stabilizing input policy, we show that temporally-dependent samples can be approximated by i. i. d.
Quickly Finding the Best Linear Model in High Dimensions
We propose projected gradient descent (PGD) algorithm to estimate the population minimizer in the finite sample regime.
