The problem of maximizing the weighted average of performance certificates in the presence of uncertainty about the true system state is shown to be equivalent to an optimization over nested prediction sets, connecting to the literature on conformal prediction and extending prior art on single-level risk-averse decision making.
Abstract
Many applications in engineering, including wireless broadcasting, require designs that provide performance certificates at different target outage levels. This paper studies the problem of maximizing the weighted average of such certificates in the presence of uncertainty about the true system state. The problem is shown to be equivalent to an optimization over nested prediction sets, connecting to the literature on conformal prediction and extending prior art on single-level risk-averse decision making. Furthermore, we derive a dual formulation that decouples optimization across input values. Numerical experiments on a diversity-based wireless transmission system illustrate the cost of enforcing multi-level certificates with a single shared policy and trace the Pareto trade-off between multiple reliability levels.
This work characterize the optimal policy under known distributions, and shows that it reduces to a prediction set-based solution for the CVaR, which provides an operational interpretation of conformal prediction-type prediction sets.
This work proposes an algorithm that not only tackles the issue of sample average of performance estimates generated by the optimization algorithm, but also provides a consistent online estimator for the variance of the performance.
Teng Lian, Jian-Qiang Hu, Yu-Hang Wu et al.· Naval Research Logistics· 0 citations
Network scheduling must balance performance with safety considerations, particularly in critical infrastructure systems. While traditional approaches either rely on detailed system models or simplified binary safety indicators, both have limitations in uncertain environments. We propose a novel model-free approach for...
Risk is a critical consideration in many energy-related problems, where stakeholders are often sensitive to rare but costly events. However, traditional online algorithms typically optimize expected cost and can perform poorly in such risk-sensitive settings. Motivated by this challenge, we study the design of algorith...
Lukas Himmelreich, Nicolas H. Christianson, A. Wierman· ACM SIGMETRICS Performance E...· 1 citation
Many real life problems require making decisions that lead to benefit allocations across multiple entities, hence they can be cast as special types of multiobjective programming problems. In such settings, the decision makers often have fairness concerns alongside efficiency concerns. We propose a method for achievin...
Ozlem Karsu, Barkın Ekiz· Annals of Operations Researc...· 0 citations
High penetration of distributed energy resources and uncertain loads creates financial and operational challenges for distribution networks. To address these challenges, this paper proposes a risk-averse, two-stage stochastic coordination framework for a distribution network interacting with the main grid. The day-ahea...
Chao Lu, Yu Lu, Meng-Hua Deng et al.· Frontiers in Energy Research· 0 citations
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
