Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
1908.08729
Cited By
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
23 August 2019
Daniel Kuhn
Peyman Mohajerin Esfahani
Viet Anh Nguyen
Soroosh Shafieezadeh-Abadeh
OOD
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning"
50 / 183 papers shown
RoME: Domain-Robust Mixture-of-Experts for MILP Solution Prediction across Domains
Tianle Pu
Zijie Geng
Haoyang Liu
Shixuan Liu
Jie Wang
Li Zeng
Chao Chen
Changjun Fan
179
8
0
04 Nov 2025
Robust Decision Making with Partially Calibrated Forecasts
Shayan Kiyani
Hamed Hassani
George Pappas
Aaron Roth
191
0
0
27 Oct 2025
Distributionally Robust Optimization via Diffusion Ambiguity Modeling
Jiaqi Wen
Jianyi Yang
136
2
0
26 Oct 2025
Distributionally Robust Causal Abstractions
Yorgos Felekis
Theodoros Damoulas
Paris Giampouras
OOD
194
1
0
06 Oct 2025
Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints
Oscar Leong
Eliza O'Reilly
Yong Sheng Soh
OOD
216
0
0
03 Oct 2025
Mitigating Spurious Correlation via Distributionally Robust Learning with Hierarchical Ambiguity Sets
Sung Ho Jo
Seonghwi Kim
Minwoo Chae
154
1
0
03 Oct 2025
Predicting Effects, Missing Distributions: Evaluating LLMs as Human Behavior Simulators in Operations Management
Runze Zhang
Xiaowei Zhang
Mingyang Zhao
158
1
0
30 Sep 2025
Group Distributionally Robust Machine Learning under Group Level Distributional Uncertainty
Xenia Konti
Yi Shen
Zifan Wang
Karl H. Johansson
Michael J. Pencina
Nicoleta J. Economou-Zavlanos
Michael M. Zavlanos
OOD
181
1
0
10 Sep 2025
Distributional Adversarial Attacks and Training in Deep Hedging
Guangyi He
Tobias Sutter
Lukas Gonon
AAML
191
0
0
20 Aug 2025
Strategically Robust Game Theory via Optimal Transport
Nicolas Lanzetti
Sylvain Fricker
S. Bolognani
Florian Dorfler
Dario Paccagnan
190
4
0
21 Jul 2025
Unregularized limit of stochastic gradient method for Wasserstein distributionally robust optimization
Tam Le
228
1
0
05 Jun 2025
Distributionally Robust Wireless Semantic Communication with Large AI Models
Long Tan Le
Senura Hansaja Wanasekara
Zerun Niu
Yansong Shi
Phuong Vo
Phuong Vo
Walid Saad
Zhu Han
Choong Seon Hong
Choong Seon Hong
208
1
0
28 May 2025
Causality-Inspired Robustness for Nonlinear Models via Representation Learning
Marin Šola
Peter Bühlmann
Xinwei Shen
OOD
414
3
0
19 May 2025
Decision Making under Model Misspecification: DRO with Robust Bayesian Ambiguity Sets
Charita Dellaporta
Patrick O'Hara
Theodoros Damoulas
360
1
0
06 May 2025
Wasserstein Distributionally Robust Regret Optimization
Lukas-Benedikt Fiechtner
Jose Blanchet
357
5
0
15 Apr 2025
Provable Robust Overfitting Mitigation in Wasserstein Distributionally Robust Optimization
International Conference on Learning Representations (ICLR), 2025
Shuang Liu
Yihan Wang
Yifan Zhu
Yibo Miao
Xiao-Shan Gao
490
0
0
06 Mar 2025
Enhancing Distributional Robustness in Principal Component Analysis by Wasserstein Distances
Lei Wang
Xin Liu
Xiaojun Chen
382
0
0
04 Mar 2025
A stochastic smoothing framework for nonconvex-nonconcave min-sum-max problems with applications to Wasserstein distributionally robust optimization
Wei Liu
Muhammad Khan
Gabriel Mancino-Ball
Yangyang Xu
274
3
0
24 Feb 2025
Achievable distributional robustness when the robust risk is only partially identified
Neural Information Processing Systems (NeurIPS), 2025
Julia Kostin
Nicola Gnecco
Fanny Yang
331
4
0
04 Feb 2025
Universal generalization guarantees for Wasserstein distributionally robust models
International Conference on Learning Representations (ICLR), 2024
Tam Le
Jérome Malick
OOD
481
7
0
28 Jan 2025
Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise
Neural Information Processing Systems (NeurIPS), 2024
Shuyao Li
Sushrut Karmalkar
Ilias Diakonikolas
Jelena Diakonikolas
OOD
259
4
0
11 Nov 2024
skwdro
\texttt{skwdro}
skwdro
: a library for Wasserstein distributionally robust machine learning
Florian Vincent
Waïss Azizian
F. Iutzeler
J. Malick
OOD
394
5
0
28 Oct 2024
Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts
Amir Najafi
Samin Mahdizadeh Sani
Farzan Farnia
OOD
FedML
408
0
0
26 Oct 2024
Optimal Transportation by Orthogonal Coupling Dynamics
Mohsen Sadr
Peyman Mohajerin Esfehani
Hossein Gorji
OT
553
1
0
10 Oct 2024
Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing
Andreas Loukas
Karolis Martinkus
Ed Wagstaff
Kyunghyun Cho
OOD
335
3
0
08 Oct 2024
Zeroth-Order Stochastic Mirror Descent Algorithms for Minimax Excess Risk Optimization
Zhihao Gu
Zi Xu
333
1
0
22 Aug 2024
On the KL-Divergence-based Robust Satisficing Model
Haojie Yan
Minglong Zhou
Jiayi Guo
265
2
0
17 Aug 2024
Generalizing Few Data to Unseen Domains Flexibly Based on Label Smoothing Integrated with Distributionally Robust Optimization
Yangdi Wang
Zhi-Hai Zhang
Su Xiu Xu
Wenming Guo
279
0
0
09 Aug 2024
RCDM: Enabling Robustness for Conditional Diffusion Model
Weifeng Xu
Xiang Zhu
Xiaoyong Li
AAML
286
0
0
05 Aug 2024
Addressing Behavior Model Inaccuracies for Safe Motion Control in Uncertain Dynamic Environments
Minjun Sung
Hunmin Kim
N. Hovakimyan
130
1
0
26 Jul 2024
Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls
Aras Selvi
Eleonora Kreacic
Mohsen Ghassemi
Vamsi K. Potluru
T. Balch
Manuela Veloso
558
2
0
18 Jul 2024
Decision-Focused Evaluation of Worst-Case Distribution Shift
Kevin Ren
Yewon Byun
Bryan Wilder
OffRL
309
4
0
04 Jul 2024
Adaptive Preference Scaling for Reinforcement Learning with Human Feedback
Ilgee Hong
Zichong Li
Alexander Bukharin
Yixiao Li
Haoming Jiang
Tianbao Yang
Tuo Zhao
270
15
0
04 Jun 2024
A Geometric Unification of Distributionally Robust Covariance Estimators: Shrinking the Spectrum by Inflating the Ambiguity Set
Man-Chung Yue
Yves Rychener
Daniel Kuhn
Viet Anh Nguyen
343
3
0
30 May 2024
Sharp analysis of out-of-distribution error for "importance-weighted" estimators in the overparameterized regime
Kuo-Wei Lai
Vidya Muthukumar
271
3
0
10 May 2024
Stability Evaluation via Distributional Perturbation Analysis
Jose H. Blanchet
Peng Cui
Jiajin Li
Tianyu Wang
269
2
0
06 May 2024
Uncertainty Quantification on Graph Learning: A Survey
Chao Chen
Chenghua Guo
Rui Xu
Xiangwen Liao
Xi Zhang
Sihong Xie
Hui Xiong
Mohit Bansal
AI4CE
450
3
0
23 Apr 2024
Approximate Algorithms For
k
k
k
-Sparse Wasserstein Barycenter With Outliers
Qingyuan Yang
Hu Ding
228
3
0
20 Apr 2024
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation
Hongwei Tan
Ziruo Cai
Marcelo Pereyra
Subhadip Mukherjee
Junqi Tang
Carola-Bibiane Schönlieb
SSL
431
3
0
08 Apr 2024
On the Benefits of Over-parameterization for Out-of-Distribution Generalization
Yifan Hao
Yong Lin
Difan Zou
Tong Zhang
OODD
OOD
263
7
0
26 Mar 2024
Automatic Outlier Rectification via Optimal Transport
Jose H. Blanchet
Jiajin Li
Markus Pelger
Greg Zanotti
241
4
0
21 Mar 2024
A Distributionally Robust Estimator that Dominates the Empirical Average
Nikolas P. Koumpis
Dionysis Kalogerias
244
0
0
16 Feb 2024
Sampling from the Mean-Field Stationary Distribution
Annual Conference Computational Learning Theory (COLT), 2024
Yunbum Kook
Matthew Shunshi Zhang
Sinho Chewi
Murat A. Erdogdu
Mufan Li
506
11
0
12 Feb 2024
Conformal Predictive Programming for Chance Constrained Optimization
Yiqi Zhao
Xinyi Yu
Matteo Sesia
Lars Lindemann
Lars Lindemann
462
9
0
12 Feb 2024
Cross-modality debiasing: using language to mitigate sub-population shifts in imaging
Yijiang Pang
Hoang Bao
Jiayu Zhou
359
1
0
02 Feb 2024
Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization
Neural Information Processing Systems (NeurIPS), 2024
Nicola Bariletto
Nhat Ho
OOD
383
2
0
28 Jan 2024
AdvST: Revisiting Data Augmentations for Single Domain Generalization
Guangtao Zheng
Mengdi Huai
Aidong Zhang
332
31
0
20 Dec 2023
f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization
International Conference on Learning Representations (ICLR), 2023
Sina Baharlouei
Shivam Patel
Meisam Razaviyayn
441
4
0
06 Dec 2023
Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space
Annual Conference Computational Learning Theory (COLT), 2023
Yiheng Jiang
Sinho Chewi
Aram-Alexandre Pooladian
655
17
0
05 Dec 2023
Stochastic Vision Transformers with Wasserstein Distance-Aware Attention
Franciskus Xaverius Erick
Mina Rezaei
Johanna P. Müller
Bernhard Kainz
248
0
0
30 Nov 2023
1
2
3
4
Next
Page 1 of 4
