Robust Wasserstein Profile Inference and Applications to Machine Learning

18 October 2016

Papers citing "Robust Wasserstein Profile Inference and Applications to Machine Learning"

50 / 55 papers shown

Title
Wasserstein Distributionally Robust Nonparametric Regression Changyu Liu Yuling Jiao Junhui Wang Jian Huang OOD 34 0 0 12 May 2025
ReLU integral probability metric and its applications Yuha Park Kunwoong Kim Insung Kong Yongdai Kim 48 0 0 26 Apr 2025
Nonsmooth Nonconvex-Nonconcave Minimax Optimization: Primal-Dual Balancing and Iteration Complexity Analysis Jiajin Li Lingling Zhu Anthony Man-Cho So 54 4 0 17 Jan 2025
New Perspectives on Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization Soroosh Shafieezadeh-Abadeh Liviu Aolaritei Florian Dorfler Daniel Kuhn 66 20 0 31 Dec 2024
Alternating minimization for square root principal component pursuit Shengxiang Deng Xudong Li Yangjing Zhang 42 0 0 31 Dec 2024
Distributionally Robust Optimization Daniel Kuhn Soroosh Shafiee W. Wiesemann 38 0 0 04 Nov 2024
Evaluating Model Performance Under Worst-case Subpopulations Mike Li Hongseok Namkoong Shangzhou Xia 48 17 0 01 Jul 2024
Rejection via Learning Density Ratios Alexander Soen Hisham Husain Philip Schulz Vu-Linh Nguyen 58 2 0 29 May 2024
On Sinkhorn's Algorithm and Choice Modeling Zhaonan Qu Alfred Galichon Johan Ugander Johan Ugander 36 3 0 30 Sep 2023
Estimation Beyond Data Reweighting: Kernel Method of Moments Heiner Kremer Yassine Nemmour Bernhard Schölkopf Jia-Jie Zhu 36 7 0 18 May 2023
Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning Yiling Xie X. Huo 24 2 0 27 Mar 2023
Statistical Limit Theorems in Distributionally Robust Optimization Jose H. Blanchet A. Shapiro 28 13 0 27 Mar 2023
Diagnosing Model Performance Under Distribution Shift Tiffany Cai Hongseok Namkoong Steve Yadlowsky 37 27 0 03 Mar 2023
Learning Against Distributional Uncertainty: On the Trade-off Between Robustness and Specificity Shixiong Wang Haowei Wang Xinke Li Jean Honorio OOD 65 1 0 31 Jan 2023
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees Songkai Xue Yuekai Sun Mikhail Yurochkin FaML 13 0 0 15 Jan 2023
On adversarial robustness and the use of Wasserstein ascent-descent dynamics to enforce it Camilo A. Garcia Trillos Nicolas García Trillos 31 5 0 09 Jan 2023
A Distributionally Robust Optimization Framework for Extreme Event Estimation Yuanlu Bai H. Lam Xinyu Zhang 34 4 0 03 Jan 2023
Distributional Robustness Bounds Generalization Errors Shixiong Wang Haowei Wang OOD 35 4 0 20 Dec 2022
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints Jiajin Li Si-Jian Lin Jose H. Blanchet Viet Anh Nguyen OOD 47 11 0 04 Oct 2022
An Optimal Transport Approach to Personalized Federated Learning Farzan Farnia Amirhossein Reisizadeh Ramtin Pedarsani Ali Jadbabaie OT OOD FedML 31 12 0 06 Jun 2022
On the Generalization of Wasserstein Robust Federated Learning Tung Nguyen Tuan Dung Nguyen Long Tan Le Canh T. Dinh N. H. Tran OOD FedML 29 6 0 03 Jun 2022
The Multimarginal Optimal Transport Formulation of Adversarial Multiclass Classification Nicolas García Trillos Matt Jacobs Jakwang Kim OT 37 23 0 27 Apr 2022
An improved central limit theorem and fast convergence rates for entropic transportation costs E. del Barrio Alberto González Sanz Jean-Michel Loubes Jonathan Niles-Weed OT 34 32 0 19 Apr 2022
$Distributionally Robust Bayesian Optimization with $\varphi$-divergences$ Distributionally Robust Bayesian Optimization with $\varphi$ -divergences Hisham Husain Vu-Linh Nguyen Anton Van Den Hengel 40 13 0 04 Mar 2022
Global-Local Regularization Via Distributional Robustness Hoang Phan Trung Le Trung-Nghia Phung Tu Bui Nhat Ho Dinh Q. Phung OOD 22 12 0 01 Mar 2022
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training Tu Bui Trung Le Quan Hung Tran He Zhao Dinh Q. Phung AAML OOD 39 42 0 27 Feb 2022
Human Imperceptible Attacks and Applications to Improve Fairness Xinru Hua Huanzhong Xu Jose H. Blanchet V. Nguyen AAML 27 3 0 30 Nov 2021
The Geometry of Adversarial Training in Binary Classification Leon Bungert Nicolas García Trillos Ryan W. Murray AAML 32 23 0 26 Nov 2021
Integrated Conditional Estimation-Optimization Sirui Chen Paul Grigas Zuo‐Jun Max Shen CML 35 25 0 24 Oct 2021
Solving Multistage Stochastic Linear Programming via Regularized Linear Decision Rules: An Application to Hydrothermal Dispatch Planning Felipe Nazaré A. Street 20 5 0 07 Oct 2021
Sinkhorn Distributionally Robust Optimization Jie Wang Rui Gao Yao Xie 46 35 0 24 Sep 2021
On the regularized risk of distributionally robust learning over deep neural networks Camilo A. Garcia Trillos Nicolas García Trillos OOD 45 10 0 13 Sep 2021
Distributionally Robust Learning Ruidi Chen I. Paschalidis OOD 25 65 0 20 Aug 2021
Poisoning Attack against Estimating from Pairwise Comparisons Ke Ma Qianqian Xu Jinshan Zeng Xiaochun Cao Qingming Huang AAML 24 22 0 05 Jul 2021
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation Agnieszka Słowik Léon Bottou FaML 45 19 0 17 Jun 2021
Robust Hypothesis Testing with Wasserstein Uncertainty Sets Liyan Xie Rui Gao Yao Xie OOD 42 9 0 29 May 2021
Adversarially Robust Kernel Smoothing Jia-Jie Zhu Christina Kouridi Yassine Nemmour Bernhard Schölkopf 28 7 0 16 Feb 2021
Unbiased Gradient Estimation for Distributionally Robust Learning Soumyadip Ghosh M. Squillante OOD 23 7 0 22 Dec 2020
Adversarial Classification: Necessary conditions and geometric flows Nicolas García Trillos Ryan W. Murray AAML 37 19 0 21 Nov 2020
Robust Validation: Confident Predictions Even When Distributions Shift Maxime Cauchois Suyash Gupta Alnur Ali John C. Duchi OOD 24 90 0 10 Aug 2020
Distributionally Robust Losses for Latent Covariate Mixtures John C. Duchi Tatsunori Hashimoto Hongseok Namkoong 18 79 0 28 Jul 2020
A Distributionally Robust Approach to Fair Classification Bahar Taşkesen Viet Anh Nguyen Daniel Kuhn Jose H. Blanchet FaML 28 61 0 18 Jul 2020
Distributionally Robust Weighted $k$ -Nearest Neighbors Shixiang Zhu Liyan Xie Minghe Zhang Rui Gao Yao Xie OOD 21 7 0 07 Jun 2020
Principled learning method for Wasserstein distributionally robust optimization with local perturbations Yongchan Kwon Wonyoung Hedge Kim Joong-Ho Won M. Paik 19 12 0 05 Jun 2020
Distributional Robustness and Regularization in Reinforcement Learning E. Derman Shie Mannor 27 44 0 05 Mar 2020
A First-Order Algorithmic Framework for Wasserstein Distributionally Robust Logistic Regression Jiajin Li Sen Huang Anthony Man-Cho So OOD 30 12 0 28 Oct 2019
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning Daniel Kuhn Peyman Mohajerin Esfahani Viet Anh Nguyen Soroosh Shafieezadeh-Abadeh OOD 25 389 0 23 Aug 2019
Distributionally Robust Optimization: A Review Hamed Rahimian Sanjay Mehrotra 19 115 0 13 Aug 2019
Confidence Regions in Wasserstein Distributionally Robust Estimation Jose H. Blanchet Karthyek Murthy Nian Si OOD 21 57 0 04 Jun 2019
Learning Models with Uniform Performance via Distributionally Robust Optimization John C. Duchi Hongseok Namkoong OOD 19 402 0 20 Oct 2018