Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …

In this paper, we study a class of stochastic optimization problems, referred to as the
conditional stochastic optimization (CSO), in the form of x∈XE_ξf_ξ(E_η|ξg_η(x,ξ)), which …

This paper studies mean-risk portfolio optimization models using the conditional value-at-
risk (CVaR) as a risk measure. We also employ a cardinality constraint for limiting the …

The theoretical and empirical performance of Empirical Risk Minimization (ERM) often
suffers when loss functions are poorly behaved with large Lipschitz moduli and spurious …

This paper studies sample average approximation (SAA) and its simple regularized variation
in solving convex or strongly convex stochastic programming problems. Under heavy-tailed …

This paper studies sample average approximation (SAA) in solving convex or strongly
convex stochastic programming (SP) problems. Under some common regularity conditions …

Abstract Sample Average Approximation (SAA) is used to approximately solve stochastic
optimization problems. In practice, SAA requires much fewer samples than predicted by …

We investigate the feasibility of sample average approximation (SAA) for general stochastic
optimization problems, including two-stage stochastic programs without relatively complete …

High-dimensional statistical learning (HDSL) has wide applications in data analysis,
operations research, and decision making. Despite the availability of multiple theoretical …

Stochastic programming has been widely used in various application scenarios and
theoretical research works. However, these excellent methods depend on specific explicit …