Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Xie, Yiling, Huo, Xiaoming |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Asymptotic Behavior of Adversarial Training Estimator under $\ell_\infty$-Perturbation
by: Xie, Yiling, et al.
Published: (2024)
by: Xie, Yiling, et al.
Published: (2024)
Wasserstein Distributionally Robust Online Learning
by: Chen, Guixian, et al.
Published: (2026)
by: Chen, Guixian, et al.
Published: (2026)
A Uniform Concentration Inequality for Kernel-Based Two-Sample Statistics
by: Ni, Yijin, et al.
Published: (2024)
by: Ni, Yijin, et al.
Published: (2024)
Distance-Matrix Wasserstein Statistics for Scalable Gromov--Wasserstein Learning
by: Xu, Ao, et al.
Published: (2026)
by: Xu, Ao, et al.
Published: (2026)
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits
by: Shen, Yi, et al.
Published: (2023)
by: Shen, Yi, et al.
Published: (2023)
Minimax Statistical Estimation under Wasserstein Contamination
by: Chao, Patrick, et al.
Published: (2023)
by: Chao, Patrick, et al.
Published: (2023)
Robust Estimation under the Wasserstein Distance
by: Nietert, Sloan, et al.
Published: (2023)
by: Nietert, Sloan, et al.
Published: (2023)
Online Covariance Estimation in Averaged SGD: Improved Batch-Mean Rates and Minimax Optimality via Trajectory Regression
by: Ni, Yijin, et al.
Published: (2026)
by: Ni, Yijin, et al.
Published: (2026)
Wasserstein Distributionally Robust Regret Optimization
by: Fiechtner, Lukas-Benedikt, et al.
Published: (2025)
by: Fiechtner, Lukas-Benedikt, et al.
Published: (2025)
Optimizing Computational-Statistical Runtime for Wasserstein Distance Estimation
by: Jacobs, Peter Matthew, et al.
Published: (2026)
by: Jacobs, Peter Matthew, et al.
Published: (2026)
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
by: Kuhn, Daniel, et al.
Published: (2019)
by: Kuhn, Daniel, et al.
Published: (2019)
Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning
by: Ni, Yijin, et al.
Published: (2025)
by: Ni, Yijin, et al.
Published: (2025)
Knowledge-Guided Wasserstein Distributionally Robust Optimization
by: Wang, Zitao, et al.
Published: (2025)
by: Wang, Zitao, et al.
Published: (2025)
Wasserstein Distributionally Robust Multiclass Support Vector Machine
by: Ibrahim, Michael, et al.
Published: (2024)
by: Ibrahim, Michael, et al.
Published: (2024)
Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge
by: Wang, Jingge, et al.
Published: (2022)
by: Wang, Jingge, et al.
Published: (2022)
Wasserstein Distributionally Robust Risk-Sensitive Estimation via Conditional Value-at-Risk
by: Taha, Feras Al, et al.
Published: (2026)
by: Taha, Feras Al, et al.
Published: (2026)
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances
by: Wang, Jie, et al.
Published: (2024)
by: Wang, Jie, et al.
Published: (2024)
Distributional Reinforcement Learning with Regularized Wasserstein Loss
by: Sun, Ke, et al.
Published: (2022)
by: Sun, Ke, et al.
Published: (2022)
Wasserstein Distributionally Robust Bayesian Optimization with Continuous Context
by: Micheli, Francesco, et al.
Published: (2025)
by: Micheli, Francesco, et al.
Published: (2025)
Wasserstein Distributionally Robust Shallow Convex Neural Networks
by: Pallage, Julien, et al.
Published: (2024)
by: Pallage, Julien, et al.
Published: (2024)
On Generalization and Regularization via Wasserstein Distributionally Robust Optimization
by: Wu, Qinyu, et al.
Published: (2022)
by: Wu, Qinyu, et al.
Published: (2022)
Wasserstein Distributionally Robust Estimation in High Dimensions: Performance Analysis and Optimal Hyperparameter Tuning
by: Aolaritei, Liviu, et al.
Published: (2022)
by: Aolaritei, Liviu, et al.
Published: (2022)
Towards Robust Multimodal Learning in the Open World
by: Huo, Fushuo
Published: (2025)
by: Huo, Fushuo
Published: (2025)
Mean and Variance Estimation Complexity in Arbitrary Distributions via Wasserstein Minimization
by: Iverson, Valentio, et al.
Published: (2025)
by: Iverson, Valentio, et al.
Published: (2025)
Kernel Selection is Model Selection: A Unified Complexity-Penalized Approach for MMD Two-Sample Tests
by: Ni, Yijin, et al.
Published: (2026)
by: Ni, Yijin, et al.
Published: (2026)
Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes
by: Ko, Hyunouk, et al.
Published: (2024)
by: Ko, Hyunouk, et al.
Published: (2024)
FDR-SVM: A Federated Distributionally Robust Support Vector Machine via a Mixture of Wasserstein Balls Ambiguity Set
by: Ibrahim, Michael, et al.
Published: (2024)
by: Ibrahim, Michael, et al.
Published: (2024)
Provable Robust Overfitting Mitigation in Wasserstein Distributionally Robust Optimization
by: Liu, Shuang, et al.
Published: (2025)
by: Liu, Shuang, et al.
Published: (2025)
Efficient Distribution Learning with Error Bounds in Wasserstein Distance
by: Figueiredo, Eduardo, et al.
Published: (2026)
by: Figueiredo, Eduardo, et al.
Published: (2026)
Enhancing Distributional Robustness in Principal Component Analysis by Wasserstein Distances
by: Wang, Lei, et al.
Published: (2025)
by: Wang, Lei, et al.
Published: (2025)
A New Robust Partial $p$-Wasserstein-Based Metric for Comparing Distributions
by: Raghvendra, Sharath, et al.
Published: (2024)
by: Raghvendra, Sharath, et al.
Published: (2024)
Wasserstein-regularized Conformal Prediction under General Distribution Shift
by: Xu, Rui, et al.
Published: (2025)
by: Xu, Rui, et al.
Published: (2025)
Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback
by: Wang, Yikai, et al.
Published: (2026)
by: Wang, Yikai, et al.
Published: (2026)
Learning U-Statistics with Active Inference
by: Wang, Xiaoning, et al.
Published: (2026)
by: Wang, Xiaoning, et al.
Published: (2026)
Extended Wasserstein-GAN Approach to Causal Distribution Learning: Density-Free Estimation and Minimax Optimality
by: Tamano, Shu, et al.
Published: (2026)
by: Tamano, Shu, et al.
Published: (2026)
Fast Estimation of Wasserstein Distances via Regression on Sliced Wasserstein Distances
by: Nguyen, Khai, et al.
Published: (2025)
by: Nguyen, Khai, et al.
Published: (2025)
Wasserstein Distributionally Robust Nonparametric Regression
by: Liu, Changyu, et al.
Published: (2025)
by: Liu, Changyu, et al.
Published: (2025)
Wasserstein Distributionally Robust Optimization Through the Lens of Structural Causal Models and Individual Fairness
by: Ehyaei, Ahmad-Reza, et al.
Published: (2025)
by: Ehyaei, Ahmad-Reza, et al.
Published: (2025)
Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls
by: Selvi, Aras, et al.
Published: (2024)
by: Selvi, Aras, et al.
Published: (2024)
Tight Robustness Certificates and Wasserstein Distributional Attacks for Deep Neural Networks
by: Le, Bach C., et al.
Published: (2025)
by: Le, Bach C., et al.
Published: (2025)
Similar Items
-
Asymptotic Behavior of Adversarial Training Estimator under $\ell_\infty$-Perturbation
by: Xie, Yiling, et al.
Published: (2024) -
Wasserstein Distributionally Robust Online Learning
by: Chen, Guixian, et al.
Published: (2026) -
A Uniform Concentration Inequality for Kernel-Based Two-Sample Statistics
by: Ni, Yijin, et al.
Published: (2024) -
Distance-Matrix Wasserstein Statistics for Scalable Gromov--Wasserstein Learning
by: Xu, Ao, et al.
Published: (2026) -
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits
by: Shen, Yi, et al.
Published: (2023)