4 accepted papers
This paper investigates distributionally robust filtering for state-space models subject to exogenous disturbances in state evolution and observation processes. The joint probability distribution of the disturbance process over an arbitrary horizon is unknown but is assumed to reside within a Wasser…
We study the infinite-horizon distributionally robust (DR) control of linear systems with quadratic costs, where disturbances have unknown, possibly time-correlated distribution within a Wasserstein-2 ambiguity set. We aim to minimize the worst-case expected regret—the excess cost of a causal policy…
This paper studies uncertainty set estimation for unknown linear systems. Uncertainty sets are crucial for the quality of robust control since they directly influence the conservativeness of the control design. Departing from the confidence region analysis of least squares estimation, this paper foc…
The stochastic mirror descent (SMD) algorithm is a general class of training algorithms that utilizes a mirror potential to influence the implicit bias of the training algorithm and includes stochastic gradient descent (SGD) as a special case. In this paper, we explore the performance of the SMD on…