Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling.
The tremendous potential benefits of SA are, however, yet to be fully realized, both for …

Value-at-risk (VaR) and conditional value-at-risk (CVaR) are two widely used risk measures
of large losses and are employed in the financial industry for risk management purposes. In …

In risk-averse reinforcement learning (RL), the goal is to optimize some risk measure of the
returns. A risk measure often focuses on the worst returns out of the agent's experience. As a …

Abstract Conditional Value at Risk (CVaR) is a prominent risk measure that is being used
extensively in various domains such as finance. In this work we present a new formula for …

Abstract Conditional Value at Risk (CVaR) is a prominent risk measure that is being used
extensively in various domains. We develop a new formula for the gradient of the CVaR in …

We consider Bayesian optimization of objective functions of the form $\rho [F (x, W)] $, where
$ F $ is a black-box expensive-to-evaluate function and $\rho $ denotes either the VaR or …

We study contextual stochastic optimization problems, where we leverage rich auxiliary
observations (eg, product characteristics) to improve decision making with uncertain …

When there is parameter uncertainty in the constraints of a convex optimization problem, it is
natural to formulate the problem as a joint chance constrained program (JCCP), which …

Simulation optimization is a widely used tool in the analysis and optimization of complex
stochastic systems. The majority of the previous works on simulation optimization rely …

The integration of renewable power can pose uncertainty to power system operation,
causing operational risk like power imbalance or line congestion. To improve the …