Residuals-based distributionally robust optimization with covariate information
From MaRDI portal
Publication:6608038
DOI10.1007/s10107-023-02014-7MaRDI QIDQ6608038
Rohit Kannan, Güzin Bayraksan, James R. Luedtke
Publication date: 19 September 2024
Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)
convergence ratelarge deviationscovariatesmachine learningWasserstein distancedistributionally robust optimizationphi-divergencesdata-driven stochastic programming
Cites Work
- On the rate of convergence in Wasserstein distance of the empirical measure
- Generating random correlation matrices based on vines and extended onion method
- Monte Carlo bounding techniques for determinig solution quality in stochastic programs
- Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations
- Robust sample average approximation
- Weak convergence and empirical processes. With applications to statistics
- Bootstrap robust prescriptive analytics
- Distributionally robust stochastic programs with side information based on trimmings
- Predictive stochastic programming
- Frameworks and results in distributionally robust optimization
- Dynamic optimization with side information
- Distributionally robust optimization with correlated data from vector autoregressive processes
- Tractable reformulations of two-stage distributionally robust linear programs over the type-\(\infty\) Wasserstein ball
- Robust Sensitivity Analysis for Stochastic Systems
- A Distributional Interpretation of Robust Optimization
- Julia: A Fresh Approach to Numerical Computing
- On the Rate of Convergence of Empirical Measures in ∞-transportation Distance
- Lectures on Stochastic Programming
- Decomposition Algorithms for Two-Stage Distributionally Robust Mixed Binary Programs
- Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach
- Conic Programming Reformulations of Two-Stage Distributionally Robust Linear Programs over Wasserstein Balls
- The Big Data Newsvendor: Practical Insights from Machine Learning
- Data-Driven Ambiguity Sets With Probabilistic Guarantees for Dynamic Processes
- Technical Note—Two-Stage Sample Robust Optimization
- Calibration of Distributionally Robust Empirical Optimization Models
- Confidence regions in Wasserstein distributionally robust estimation
- Recovering Best Statistical Guarantees via the Empirical Divergence-Based Distributionally Robust Optimization
- Robust Wasserstein profile inference and applications to machine learning
- Ambiguity in portfolio selection
- JuMP: A Modeling Language for Mathematical Optimization
- Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality
- Distributionally Robust Stochastic Optimization with Wasserstein Distance
- Wasserstein distributionally robust optimization and variation regularization
This page was built for publication: Residuals-based distributionally robust optimization with covariate information