We derive new and improved non-asymptotic deviation inequalities for the sample average
approximation (SAA) of an optimization problem. Our results give strong error probability …

Bagging is a popular ensemble technique to improve the accuracy of machine learning
models. It hinges on the well-established rationale that, by repeatedly retraining on …

Nonlinear dependence on a probability measure has recently been encountered with
increasing intensity in stochastic optimization. This type of dependence corresponds to …

We study the sample complexity of sample average approximation (SAA) and its simple
variations, referred to as the regularized SAA (RSAA), in solving convex and strongly convex …

Stochastic programming problems with probability and stochastic dominanceconstraints
belong to" deterministic" problems depending on a probabilitymeasure. Complete …

Optimization problems with stochastic dominance constraints are helpful to many real-life
applications. We can recall eg, problems of portfolio selection or problems connected with …

Nonlinear dependence on a probability measure begins to appear (last time) in a stochastic
optimization rather often. Namely, the corresponding type of problems corresponds to many …

Ensemble learning is a popular technique to improve the accuracy of machine learning
models. It hinges on the rationale that aggregating multiple weak models can lead to better …

The second order stochastic dominance (SSD) has become exceedingly popular in recent
years, due to its ability to determine the dominance of one asset over another for all risk …

Economic and financial processes are mostly simultaneously influenced by a random factor
and a decision parameter. While the random factor can be hardly influenced, the decision …