Ermolieva T., Ermoliev Y., Havlik P., Lessa-Derci-Augustynczik A., Komendantova N., Kahil T., Balkovic J., Skalsky R., Folberth C., Knopov P. S.
Connections between robust statistical estimation, robust decision making with two-stage stochastic optimization, and robust machine learning problems

The authors discuss connections between the problems of two-stage stochastic programming, robust decision-making, robust statistical estimation, and machine learning. In the conditions of uncertainty, possible extreme events and outliers, these problems require quantile-based criteria, constraints, and “goodness-of-fit” indicators. The two-stage stochastic optimization (STO) problems with quantile-based criteria can be effectively solved with the iterative stochastic quasigradient (SQG) solution algorithms. The SQG methods provide a new type of machine learning algorithms that can be effectively used for general-type nonsmooth, possibly discontinuous, and nonconvex problems, including quantile regression and neural network training. In general problems of decision-making, feasible solutions and concepts of optimality and robustness are characterized from the context of decision-making situations. Robust machine learning (ML) approaches can be integrated with disciplinary or interdisciplinary decision-making models, e.g., land use, agricultural, energy, etc., for robust decision-making in the conditions of uncertainty, increasing systemic interdependencies, and “unknown risks.”. © 2023, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords: general problems of robust decision making, machine learning, robust decision-making and statistical estimation, robust quantile regression, systemic risks, two-stage STO, uncertainties, Iterative methods, Land use, Learning algorithms, Neural networks, Risk perception, Stochastic programming, Stochastic systems, Decisions makings, General problem of robust decision making, Machine-learning, Quantile regression, Robust decision-making and statistical estimation, Robust decisions, Robust quantile regression, Statistical estimation, Stochastic optimizations, Systemic risks, Two-stage stochastic optimization, Uncertainty, Machine learning

Cite:
Ermolieva, Ermoliev, Havlik, Lessa-Derci-Augustynczik, Komendantova, Kahil, Balkovic, Skalsky, Folberth, Knopov, Wang. (2023). Connections between robust statistical estimation, robust decision-making with two-stage stochastic optimization, and robust machine learning problems. Cybernetics and Systems Analysis, 59 (3), 33–47. doi: https://doi.org/10.1007/s10559-023-00573-3 http://jnas.nbuv.gov.ua/article/UJRN-0001402507