1 citations · 1 across the 7 of their papers we have counts for
7 papers
Exponential Adaptive Smoothing and Importance Sampling for Optimization of the Conditional Value-at-Risk
Will Asness, Brendan Keith, Boyan Lazarov +2
We present a novel method for solving conditional value-at-risk (CVaR) optimization problems based on the dual representation of CVaR, which is defined as the worst-case expectatio…
The Risk Quadrangle in Optimization: An Overview with Recent Results and Extensions
Bogdan Grechuk, Anton Malandii, Terry Rockafellar +1
This paper revisits and extends the 2013 development by Rockafellar and Uryasev of the Risk Quadrangle (RQ) as a unified scheme for integrating risk management, optimization, and s…
Biased Mean Quadrangle and Applications
Anton Malandii, Stan Uryasev
This paper introduces \emph{biased mean regression}, estimating the \emph{biased mean}, i.e., , where . The approach addresses a fundamental st…
Risk Quadrangle and Robust Optimization Based on Extended -Divergence
Cheng Peng, Anton Malandii, Stan Uryasev
The Fundamental Risk Quadrangle (FRQ) is a unified framework linking risk management, statistical estimation, and optimization. Distributionally robust optimization (DRO) based on…
Divergence Based Quadrangle and Applications
Anton Malandii, Siddhartha Gupte, Cheng Peng +1
This paper introduces a novel framework for assessing risk and decision-making in the presence of uncertainty, the \emph{-Divergence Quadrangle}. This approach expands upon the…
Expectile Quadrangle and Applications
Viktor Kuzmenko, Anton Malandii, Stan Uryasev
The paper explores the concept of the \emph{expectile risk measure} within the framework of the Fundamental Risk Quadrangle (FRQ) theory. According to the FRQ theory, a quadrangle…
Support Vector Regression: Risk Quadrangle Framework
Anton Malandii, Stan Uryasev
This paper investigates Support Vector Regression (SVR) within the framework of the Risk Quadrangle (RQ) theory. Every RQ includes four stochastic functionals -- error, regret, ris…