In many areas of applied mathematics, decentralization of information is a ubiquitous
attribute affecting how to approach a stochastic optimization, decision and estimation, or …

This article develops a safetyanalysis method for stochastic systems that is sensitive to the
possibility and severity of rare harmful outcomes. We define risk-sensitive safe sets as …

This article is concerned with a formal verification scheme for both discrete-and continuous-
time deterministic systems with unknown mathematical models. The main target is to verify …

This work develops new algorithms with rigorous efficiency guarantees for infinite horizon
imitation learning (IL) with linear function approximation without restrictive coherence …

In this letter, we discuss the problem of optimal control for affine systems in the context of
data-driven linear programming. First, we introduce a unified framework for the fixed point …

Control and optimization of dynamical systems in the presence of stochastic uncertainty is a
mature field with a large range of applications. A comprehensive treatment of such problems …

The linear programming (LP) approach is, together with value iteration and policy iteration,
one of the three fundamental methods to solve optimal control problems in a dynamic …

The linear programming (LP) approach has a long history in the theory of approximate
dynamic programming. When it comes to computation, however, the LP approach often …

We consider large-scale Markov decision processes with an unknown cost function and
address the problem of learning a policy from a finite set of expert demonstrations. We …

This dissertation provides efficient (data-driven) techniques to design highly-reliable
stochastic hybrid systems with mathematical guarantees by bringing together …