Wasserstein Distributionally Robust Optimization

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Wasserstein distributionally robust optimization (WDRO) chooses decisions that perform well even under the worst-case data distribution within an optimal-transport neighborhood of observed data. Researchers use Wasserstein duality, convex and semidefinite reformulations, transport maps, and stochastic or iterative algorithms to make these problems tractable. Applications include robust classification and regression, clustering, portfolio decisions, control, and safety, with evaluation spanning corruption tests, out-of-sample risk, and finite-sample guarantees. A central challenge is balancing protection against distribution shift with computational cost and conservatism, especially when choosing the ambiguity radius or when the assumed transport geometry misses structural changes in the data.

Latest papers

29 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Calibrating Ambiguity Set via Diagnostic Transport for Distributionally Robust Optimization
Oct 7, 2026Wenbin Zhou, Elizabeth Cucuzzella, Shixiang ZhuDistributionally Robust Optimization Wasserstein Distributionally Robust Optimization
Heinz College of Information Systems and Public Policy, Carnegie Mellon University · Machine Learning Department, Carnegie Mellon University · Department of Statistics and Data Science, Carnegie Mellon University
Adversarial Training for Deep Hedging in Nonstationary Markets
Oct 5, 2026Philipp J. Schneider, Lukas Looser, Antoine Garin +2Adversarial Training Quantitative Finance
Risk Analytics and Optimization Chair, EPFL, Lausanne, Switzerland · University of Waterloo, Waterloo, Canada
Distributionally robust linear regression through the lens of adversarial training
Sep 30, 2026Elis Stefansson, David Vävinggren, Antônio H. RibeiroRobust Regression Distributionally Robust Optimization
Uppsala University, Sweden · Science for Life Laboratory, Sweden
Brenier Meets Adversarial Training: Optimal Transport Geometry for Robust Learning
Sep 25, 2026Alireza Abdollahpoorrostam, Ehsan Sharifian, Buse Şen +2Distribution Shift Robustness Wasserstein Distributionally Robust Optimization
EPFL · Apple
Riemannian ascent--descent for nonconvex nonconcave minimax landscapes: convergence to basin saddle points and applications to distributionally robust optimization
Sep 12, 2026Rishabh Dixit, Pranav Upadrashta, Alex CloningerMin-Max Optimization Wasserstein Distributionally Robust Optimization
Department of Mathematics, UC San Diego · Department of Mathematics and Halıcıoglu Data Science Institute, UC San Diego
A Unified Perspective on Conformal Prediction and Wasserstein Distributionally Robust Optimization for Uncertainty Quantification
Aug 30, 2026Kehan Long, Yiqi Zhao, Pol Mestres +3Distribution Shift Robustness Uncertainty Quantification
Contextual Robotics Institute, University of California San Diego, La Jolla, CA, USA · Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, CA, USA · Department of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA, USA +1
Multi-Source Wasserstein Distributionally Robust Graph Learning
Aug 20, 2026Chuansen Peng, Yifan Xia, Jinshan Zhong +1Graph Structure Learning Graph Representation Learning
School of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, China · School of Statistics and Data Science, Southwestern University of Finance and Economics, Chengdu, Sichuan, 611130, China
Robust Risk Under Evolving Uncertainty: A Wasserstein Counterpart of the Entropic Value-at-Risk
Aug 19, 2026Deep Kumar Ganguly, Jan KřetínskýRisk-Sensitive RL Wasserstein Distributionally Robust Optimization
Technical University of Munich, Germany · Masaryk University, Brno, Czech Republic
Generalization Bounds on Optimal Control for Transformer Training and Wasserstein Distributional Robustness
Jul 30, 2026Kağan Akman, Naci Saldi, Serdar YükselTransformer Wasserstein Distributionally Robust Optimization
Department of Mathematics, Bilkent University, 06800, Ankara, Turkey · Department of Mathematics and Statistics, Queen’s University, Kingston, K7L 3N8, Ontario, Canada
Robust Chance-Constrained Optimization using a Continuous Parameter Space Wasserstein-2 Ambiguity Set of Gaussian Mixtures
Jul 19, 2026Shibshankar Dey, Sanjay MehrotraChance-Constrained Optimization Gaussian Mixture Models
Department of Industrial Engineering and Management Sciences, Northwestern University
Learning Predictive Ambiguity Sets for Decision-Focused Distributionally Robust Optimization
Jul 10, 2026Junjie GuoDistributionally Robust Optimization Decision-Focused Learning
Rutgers University
Robust $Q$ -learning for mean-field control under Wasserstein uncertainty in common noise
Jun 18, 2026Mathieu Laurière, Ariel Neufeld, Kyunghyun ParkQ-Learning Wasserstein Distributionally Robust Optimization
NYU Shanghai HPC · MOE AcRF Tier 1 Grant RG109/25 · National Research Foundation of Korea
Conservative neural posterior estimation via distributionally robust training
May 27, 2026William Laplante, Yuga Hikida, Charita Dellaporta +2Simulation-Based Inference Neural Posterior Estimation
Department of Statistical Science, University College London, UK · The Alan Turing Institute, UK · Department of Computer Science, Aalto University, Finland
Provably Safe Motion Planning Under Unknown Disturbances
May 26, 2026Ibon Gracia, Qi Heng Ho, Luca Laurenti +1Sampling-Based Motion Planning Chance-Constrained Motion Planning
Department of Aerospace Engineering Sciences at the University of Colorado Boulder, CO, USA · Department of Aerospace and Ocean Engineering, Virginia Tech, VA, USA · Delft University of Technology, the Netherlands, and the Italian Institute of Artificial Intelligence, Italy
Distributionally Robust Safety Under Arbitrary Uncertainties: A Safety Filtering Approach
May 13, 2026Daniel M. Cherenson, Haejoon Lee, Taekyung Kim +1Safety-Critical Control Distributionally Robust Optimization
Department of Robotics, University of Michigan, Ann Arbor, MI, USA
Ensemble Distributionally Robust Bayesian Optimisation with Continuous Context
May 8, 2026Tigran Ramazyan, Denis DerkachBayesian Optimization Distributionally Robust Optimization
Faculty of Computer Science, HSE University, Pokrovsky Boulevard 11, Moscow, 109028, Moscow, Russia.
Distributionally-Robust Learning to Optimize
May 7, 2026Vinit Ranjan, Jisun Park, Bartolomeo StellatoLearning to Optimize Distributionally Robust Optimization
Department of Operations Research and Financial Engineering, Princeton University. · Department of Operations Research and Financial Engineering, Princeton University, and Research Institute of Mathematics, Seoul National University. · Research Institute of Mathematics, Seoul National University.
Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback
Apr 30, 2026Yikai Wang, Shang Liu, Jose BlanchetReward Over-Optimization Regret Minimization in RL
Department of Statistics and Operations Research, University of North Carolina · Imperial Business School, Imperial College London · Department of Management Science and Engineering, Stanford University
Wasserstein Distributionally Robust Risk-Sensitive Estimation via Conditional Value-at-Risk
Apr 20, 2026Feras Al Taha, Eilyan BitarDistributionally Robust Optimization Wasserstein Distributionally Robust Optimization
School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853 USA
Distributionally Robust K-Means Clustering
Apr 13, 2026Vikrant Malik, Taylan Kargin, Babak HassibiClustering Distribution Shift Robustness
Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125 USA. · California Institute of Technology, Pasadena, CA 91125 USA · Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
Statistical Guarantees for Distributionally Robust Optimization with Optimal Transport and OT-Regularized Divergences
Mar 29, 2026Jeremiah Birrell, Xiaoxi ShenDistributionally Robust Optimization Wasserstein Distributionally Robust Optimization
Department of Mathematics Texas State University San Marcos, TX, USA
Evaluating Black-Box Vulnerabilities with Wasserstein-Constrained Data Perturbations
Mar 16, 2026Adriana Laurindo Monteiro, Jean-Michel LoubesExplainability Evaluation Black-Box Model Auditing
Independent Researcher · INRIA Regalia & ANITI · Institut de Math´ematiques de Toulouse, France +1
Risk-Averse Wasserstein Distributionally Robust Online Learning
Feb 23, 2026Guixian Chen, Salar Fattahi, Soroosh ShafieeDistributionally Robust Optimization Decision-Making under Uncertainty
University of Michigan · Cornell University
Contextual Distributionally Robust Optimization with Causal and Continuous Structure
Jan 16, 2026Fenglin Zhang, Jie WangStochastic Optimization Distributionally Robust Optimization
School of Artificial Intelligence The Chinese University of Hong Kong, Shenzhen Shenzhen, 518172, China · School of Artificial Intelligence, School of Data Science The Chinese University of Hong Kong, Shenzhen Shenzhen, 518172, China
Wasserstein Robust Classification Error: Surrogate Loss Misalignment and Distributional Attacks
Oct 11, 2025Bach C. Le, Tung V. Dao, Binh T. Nguyen +1Robust Classification Adversarial Robustness
College of Engineering & Computer Science, VinUniversity, Hanoi, Vietnam
Improving Mixup Calibration with Wasserstein Distributionally Robust Optimization
Jun 22, 2025Jiaming Hu, Yeping Jin, Debarghya Mukherjee +1Image Corruption Robustness Data Augmentation
Department of Mathematics & Statistics Boston University · Department of Electrical and Computer Engineering, Division of Systems Engineering Department of Biomedical Engineering Faculty of Computing & Data Sciences Boston University
Wasserstein Distributionally Robust Regret Optimization
Apr 15, 2025Lukas-Benedikt Fiechtner, Jose BlanchetDistributionally Robust Optimization Robust Optimization
Institute of Computational and Mathematical Engineering Stanford University · Institute of Computational and Mathematical Engineering Department of Management Science and Engineering Stanford University
A stochastic smoothing framework for nonconvex-nonconcave minEmax problems with applications to Wasserstein distributionally robust optimization
Feb 24, 2025Wei Liu, Muhammad Khan, Gabriel Mancino-Ball +1Randomized Smoothing Nonconvex Stochastic Optimization
Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China · Institute for Math & AI, Wuhan (IMAI), Wuhan university, China · Department of Mathematical Sciences Rensselaer Polytechnic Institute, Troy, NY, USA
Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces
Dec 29, 2024Linglingzhi Zhu, Yunqin Zhu, Yao XieDistributionally Robust Optimization Wasserstein Distributionally Robust Optimization
1H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology

Oct 7, 2026J/K move · Enter open · S save

Calibrating Ambiguity Set via Diagnostic Transport for Distributionally Robust Optimization

Wenbin Zhou, Elizabeth Cucuzzella, Shixiang Zhu

Heinz College of Information Systems and Public Policy, Carnegie Mellon University · Machine Learning Department, Carnegie Mellon University · Department of Statistics and Data Science, Carnegie Mellon University

Distributionally Robust Optimization Wasserstein Distributionally Robust Optimization Uncertainty Calibration

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Distributionally robust optimization (DRO) protects decisions against distributional uncertainty by optimizing over an ambiguity set, but poorly aligned set geometry can require large radii and yield overly conservative decisions. We introduce diagnostic-transport DRO (DT-DRO), which uses held-out calibration data to adapt the ambiguity-set geometry to observed predictive errors. DT-DRO uses the conditional probability integral transform cumulative distribution function to diagnose systematic probability misallocation and translates this information into an outcome-level transport that jointly adjusts the ambiguity-set center and ground cost. The resulting formulation admits a computationally tractable dual reformulation. Theoretically, we derive valid ambiguity radii and decision-risk guarantees that tighten as estimation and approximation errors vanish, and show that DT-DRO can eliminate the nonvanishing robustness floor caused by model misspecification. Synthetic experiments and a power-outage application demonstrate improved decision quality, particularly under structural and tail misspecification.