Distributionally Robust Optimization

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DRO studies how to make predictions and decisions reliable when the distribution at deployment may differ from the observed data. Methods define ambiguity sets using tools such as Wasserstein distance, statistical divergences, or group-based reweighting, then solve the resulting worst-case optimization with dual formulations, gradient methods, or saddle-point algorithms. Applications include machine learning under subgroup and domain shifts, reinforcement learning, control, and decisions in areas such as healthcare and energy. A central challenge is balancing protection against distribution shift with computational cost and excessive conservatism, while choosing ambiguity sets that reflect real uncertainty and still support useful guarantees.

Latest papers

85 papers18 papers in the last four weeks, up 350% on the four weeks before. 0.1% of all new papers.

Doubly Robust Distributionally Robust Offline Contextual Pricing
Oct 22, 2026Min Xu, Xinyi Yin, Yunfan Zhang +3Off-Policy Evaluation Distributionally Robust Optimization
School of Management and Engineering, Nanjing University, Nanjing, China · School of Mathematics, Nanjing University, Nanjing, China · Stern School of Business, New York University, New York, USA
Group Distributionally Robust Optimization over Decentralized Networks
Oct 21, 2026Haomin Bai, Junyan Sun, Quan Cheng +2Group Distributionally Robust Optimization Distributionally Robust Optimization
State Key Laboratory of Novel Software Technology, Nanjing University · School of Artificial Intelligence, Nanjing University · School of Software Technology, Zhejiang University
TPO: Tri-level Distributionally Robust Learning for OOD Direct Preference Optimization
Oct 20, 2026Chengtao Jian, Kai YangLLM Alignment Direct Preference Optimization
School of Computer Science and Technology, Tongji University, Shanghai, China · Shenzhen Loop Area Institute, Shenzhen, China · Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China
Distributionally-Robust Policy Learning with Observational Data
Oct 18, 2026Debmalya MandalPolicy Learning Distribution Shift Robustness
Dept. of Computer Science University of Warwick, UK
Personalized Projected Ascent for Federated Stackelberg Optimization
Oct 15, 2026M Yashwanth, Sharannya Ghosh, Saikiran Bulusu +1Federated Learning Stochastic Gradient Descent
Indian Institute of Science, Bangalore · CISPA, Germany · IIIT-Hyderabad
Private and Distributionally Robust Federated Multiclass Classification
Oct 9, 2026Andrea P. Hernandez, Michael Ibrahim, Nagi GebraeelDifferentially Private Federated Learning Multiclass Classification
H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332-0205, USA
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
Distributionally Robust Mixture-of-Experts Training
Oct 5, 2026Xin Teng, Muxiao Li, Hongyi WenMixture-of-Experts Language Models Sparse Mixture-of-Experts
New York University Center for Data Science, NYU Shanghai
Dynamic Minimax Regret Optimization for Robust LLM Post-Training
Oct 5, 2026Chengbo Zang, Haoyu Dong, Mehmet Kerem Turkcan +3Group Distributionally Robust Optimization Dynamic Regret Minimization
Department of Electrical Engineering, Columbia University · Department of Computer Science & Engineering, University of California, Santa Cruz · Department of Civil Engineering, Columbia University
Largest Rashomon sets of decision trees for robust contextual optimization
Oct 3, 2026Lorenzo Bonasera, David PisingerDecision Tree Learning Distributionally Robust Optimization
DTU Management, Technical University of Denmark, Akademivej, Building 358, Kongens Lyngby, 2800, Denmark
Distributionally Robust Survival Models under Subpopulation Shift and Outlier Contamination
Oct 2, 2026Seonghwi Kim, Sung Ho Jo, Minwoo ChaeSubgroup Robustness Distribution Shift Robustness
Pohang University of Science and Technology
Distributionally Robust Schrödinger Bridge
Oct 1, 2026Jinhwan Sul, Panagiotis Theodoropoulos, Vincent Pacelli +2Distribution Shift Robustness Schrödinger Bridges
Department of Aerospace Engineering Georgia Institute of Technology, Atlanta, GA, USA
Parameter-Efficient Distributionally Robust Adaptation of Tabular Foundation Models under Subpopulation Shift
Oct 1, 2026Seonghwi Kim, Sung Ho Jo, Minwoo ChaeDistribution Shift Robustness Tabular Foundation Models
Pohang University of Science and Technology
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
How Much Can Reliability Drift Under a Fixed Confidence Distribution?
Sep 30, 2026Wenhao Liang, Lin Yue, Wei Emma Zhang +3Distribution Shift Robustness Probability Calibration
Adelaide University
A Comprehensive View of Fairness through Distributional Stability
Sep 29, 2026Gayane Taturyan, Charlotte Laclau, Stephan ClémenconDistribution Shift Robustness Algorithmic Fairness
LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France
Learning the Robustness Mechanism with Bilevel Optimization
Sep 28, 2026Yiyang Shen, Qihang Lin, Weiran WangDistribution Shift Robustness Group Distributionally Robust Optimization
Department of Informatics University of Iowa · Tippie College of Business University of Iowa · Department of Computer Science University of Iowa
Direct Self-Evolving Optimization: Evolving LLMs without Challenger Training
Sep 28, 2026Yuyang Deng, Yu Wang, Jiayun WangSelf-Training for Language Models Large Language Model-Guided Optimization
Accenture, Center for Advanced AI · Georgia Institute of Technology
Domain Adaptation with Target Information via Doubly-Anchored Distributionally Robust Optimization
Sep 26, 2026David Kepplinger, Anand N. VidyashankarDomain Adaptation Distributionally Robust Optimization
Department of Statistics George Mason University Fairfax, VA 22030, USA
FedeRage: Provably Convergent Agnostic Federated Learning under General Client Drift
Sep 17, 2026Herlock Rahimi, Dionysis KalogeriasFederated Learning Federated Learning Aggregation
Distributionally Robust Federated Learning with Multi-Source Data
Sep 17, 2026Yingzhu Liu, Zhongkui Li, Pengcheng You +1Distributionally Robust Optimization Non-IID Federated Learning
Department of Control Science and Systems Engineering, Peking University, Beijing, China. · Engineering and Technology Institute Groningen and the Jan C. Willems Center for Systems and Control, University of Groningen, The Netherlands.
Certified Safety Radii in Forecast-Error Space for Wasserstein Distributionally Robust Small Signal Stability-Constrained AC Optimal Power Flow via Lifted Spectrahedral Containment
Aug 31, 2026Ziqi Zhang, Xi ChenOptimal Power Flow Distributionally Robust Optimization
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
Generative Neural Networks for Sinkhorn Distributionally Robust Hypothesis Testing
Aug 24, 2026Fenglin Zhang, Teyan Liu, Jie WangStatistical Hypothesis Testing Distributionally Robust Optimization
School of Artificial Intelligence and School of Data Science, The Chinese University of Hong Kong, Shenzhen · Department of Decision Analytics and Operations, College of Business, City University of Hong Kong
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
Supply Chain Analytics: A Data-Driven Approach
Aug 17, 2026Elioth SanabriaSupply Chain Management Data-Driven Control
Statistical Properties of Robust Learning under Distributional Shifts
Aug 13, 2026Zhiyi Li, Xiaojie Mao, Yunbei Xu +1Distribution Shift Robustness Distributionally Robust Optimization
National University of Singapore · Tsinghua University · University College London
The Sample Complexity of Distributionally Robust PAC Learning under Cressie--Read Divergences
Aug 5, 2026Elad Aigner-Horev, Daniel Rosenberg, Roi WeissSample Complexity Distributionally Robust Optimization
School of Computer Science and AI Ariel University 40800 Ariel, Israel
Equitable System-Prompt Selection via Constrained Mixed-Strategy GroupDRO
Aug 5, 2026Mengyu Xu, Qiaoxin Yang, Zhihan Liu +4Group Distributionally Robust Optimization LLM Prompting
1The University of Chicago · 2SynAI Technologies, Inc. · 3Northwestern University +2
Generative Distributionally Robust Optimization
Jul 27, 2026Ziwei Zhang, Jonathan Yu-Meng Li, Zhihao JinSinkhorn Divergence Distributionally Robust Optimization
1Telfer School of Management, University of Ottawa · Department of Electrical and Computer Engineering, Western University
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

Oct 22, 2026J/K move · Enter open · S save

Doubly Robust Distributionally Robust Offline Contextual Pricing

Min Xu, Xinyi Yin, Yunfan Zhang, Yuxuan Han+2

School of Management and Engineering, Nanjing University, Nanjing, China · School of Mathematics, Nanjing University, Nanjing, China · Stern School of Business, New York University, New York, USA

Off-Policy Evaluation Distributionally Robust Optimization Policy Learning Doubly Robust Estimation

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Offline contextual pricing often relies on logged observational data, but faces challenges from distributional shifts between training and deployment environments. Distributionally robust optimization (DRO) provides a principled approach to off-policy evaluation and learning (OPE/L). However, existing methods are mostly limited to discrete actions. Recent work has explored DRO for continuous treatments using inverse propensity weighting (IPW), while such IPW-based estimators can be sensitive to the convergence rate of propensity score estimates, particularly when estimated nonparametrically, which may lead to larger estimation errors and regret. In this work, we develop a doubly robust (DR) framework for distributionally robust OPE/L in continuous pricing settings. For evaluation, we propose a localized DR estimator that addresses the computational challenges of worst-case expectations by fitting only a small number of regressions, comparable to standard non-robust DR, while achieving semiparametric efficiency under mild product rate conditions. For learning, we leverage the inherent smoothness of demand noise to handle pricing-specific discontinuities in revenue outcomes (e.g., threshold-based purchase decisions), establishing a finite-sample regret bound of

\tilde{\mathcal{O}}_p(T^{-s/(2s+1)})

for smoothness orders

s=1,2

. This bound improves upon existing regret rates in existing DRO-based off-policy learning (OPL) for continuous treatments. Extensive experiments under various levels of distribution shift validate our proposed framework.