Wasserstein Distributionally Robust Optimization

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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
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
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 +1Constrained Motion Planning Robot Safety
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 BlanchetRegret Minimization in RL Reinforcement Learning
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
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
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
Multi-Source Wasserstein Distributionally Robust Graph Learning
Date pendingChuansen 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

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Adversarial Training for Deep Hedging in Nonstationary Markets

Philipp J. Schneider, Lukas Looser, Antoine Garin, Shuhan Liu+1

Risk Analytics and Optimization Chair, EPFL, Lausanne, Switzerland · University of Waterloo, Waterloo, Canada

Adversarial Training Quantitative Finance Wasserstein Distributionally Robust Optimization Finance Algorithmic Trading

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Deep hedging learns trading policies from historical or simulated market trajectories, yet under nonstationarity these training paths may not represent future market conditions. We propose WRAP (Wasserstein-Reweighting Adversarial Perturbation), a drift-aware adversarial training framework derived from a two-budget distributionally robust optimization (DRO) formulation. The formulation is anchored to a weighted empirical reference distribution whose fixed baseline weights are chosen to balance sampling uncertainty against temporal drift. Around this reference distribution, the ambiguity set addresses two complementary forms of distributional misspecification by allowing an adversary to reweight the observed trajectories subject to a

φ

-divergence constraint and perturb their paths subject to an optimal-transport (OT) constraint. We derive a joint first-order expansion in which the leading-order increase over the nominal expected loss decomposes into a reweighting contribution determined by the dispersion of hedging losses across trajectories and a transport contribution determined by the sensitivity of the loss to path perturbations. This expansion yields an explicit finite-dimensional adversarial attack that replaces the distributional inner supremum with a tractable first-order approximation. Across stationary and nonstationary Heston dynamics and a generalized affine diffusion (GAD), the experiments show complementary benefits from reweighting and transport, with joint adversarial training providing the largest gains under nonstationarity.

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