The presence of uncertainty in a system description has always been a critical issue in
control. The main objective of Randomized Algorithms for Analysis and Control of Uncertain …

In this paper, we study randomized methods for feedback design of uncertain systems. The
first contribution is to derive the sample complexity of various constrained control problems …

A novel approach based on probability and randomization has emerged to synergize with
the standard deterministic methods for control of systems with uncertainty. The main …

Sampling from high dimensional distributions and volume approximation of convex bodies
are fundamental operations that appear in optimization, finance, engineering, artificial …

We present a stochastic descent algorithm for unconstrained optimization that is particularly
efficient when the objective function is slow to evaluate and gradients are not easily …

In this technical note, we propose new sequential randomized algorithms for convex
optimization problems in the presence of uncertainty. A rigorous analysis of the theoretical …

This paper contributes to the process of eliciting additive model scale constants in order to
support choice problems, thereby reducing the effort a decision maker (DM) needs to make …

We present two stochastic descent algorithms that apply to unconstrained optimization and
are particularly efficient when the objective function is slow to evaluate and gradients are not …

Hit-and-Run is known to be one of the best random sampling algorithms, its mixing time is
polynomial in dimension. However in practice, the number of steps required to obtain …

We introduce Reflective Hamiltonian Monte Carlo (ReHMC), an HMC-based algorithm to
sample from a log-concave distribution restricted to a convex body. The random walk is …