Robust optimization and stochastic optimization are the two main paradigms for dealing with
the uncertainty inherent in almost all real-world optimization problems. The core principle of …

In this paper we consider uncertain second-order cone (SOC) and semidefinite
programming (SDP) constraints with polyhedral uncertainty, which are in general …

Steering a nonlinear system from an initial state to a desired one is a common task in
control. While a nominal trajectory can be obtained rather systematically using a model, for …

We study two-stage stochastic optimization problems with random recourse, where the
coefficients of the adaptive decisions involve uncertain parameters. To deal with the infinite …

In this paper, we consider uncertain second-order cone (SOC) and semidefinite
programming (SDP) constraints with polyhedral uncertainty, which are in general …

In this paper, we propose a novel decentralized control method to maintain Line-of-Sight
connectivity for multi-robot networks in the presence of Guassian-distributed localization …

This paper investigates advantages of using 2-Wasserstein ambiguity sets over 1-
Wasserstein sets in two-stage distributionally robust optimization with right-hand side …

Over the past two decades, robust optimization techniques have efficiently addressed
decision problems under uncertainty, offering high assurance of feasibility without being …

We study a pessimistic stochastic bilevel program in the context of sequential two-player
games, where the leader makes a binary here-and-now decision, and the follower responds …

We study the Bandits with Stochastic Corruption problem, ie a stochastic multi-armed bandit
problem with $ k $ unknown reward distributions, which are heavy-tailed and corrupted by a …