Operations research (OR) has been at the core of decision making since World War II, and
today, business interactions on different platforms have changed business dynamics …

A large number of problems involve making decisions in an uncertain environment and,
hence, with unknown outcomes. Optimization models aimed at controlling the trade‐off …

The cryptocurrency industry has changed the human life and accelerated financial
exchange in this decade. Many investors want to invest in this industry and establish …

Purpose This study sought to develop models with good identification for adverse outcomes
in patients with heart failure (HF) and find strong factors that affect prognosis. Patients and …

In risk-sensitive learning, one aims to find a hypothesis that minimizes a risk-averse (or risk-
seeking) measure of loss, instead of the standard expected loss. In this paper, we propose to …

We consider a distributionally robust formulation of stochastic optimization problems arising
in statistical learning, where robustness is with respect to ambiguity in the underlying data …

Reliability-based design approaches via scenario optimization are driven by data thereby
eliminating the need for creating a probabilistic model of the uncertain parameters. A …

Incorporating decision-makers' risk preferences into data-driven newsvendor models and
developing machine learning methods to solve the models are the challenging problems …

In this paper we present novel data-driven optimization models for Support Vector Machines
(SVM), with the aim of linearly separating two sets of points that have non-disjoint convex …

Abstract Conditional Value at Risk ($\textsc {CVaR} $) is a``coherent risk measure''which
generalizes expectation (reduced to a boundary parameter setting). Widely used in …