Cover Image

Enhancing Distributional Robustness in Principal Component Analysis by Wasserstein Distances

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Lei, Liu, Xin, Chen, Xiaojun
Format:	Preprint
Published:	2025
Subjects:	Optimization and Control Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Relaxed Wasserstein Distance Formulation for Mixtures of Radially Contoured Distributions
by: Chen, Keyu, et al.
Published: (2025)

Wasserstein Distributionally Robust Online Learning
by: Chen, Guixian, et al.
Published: (2026)

Wasserstein Distributionally Robust Regret Optimization
by: Fiechtner, Lukas-Benedikt, et al.
Published: (2025)

A Support-Set Algorithm for Optimization Problems with Nonnegative and Orthogonal Constraints
by: Wang, Lei, et al.
Published: (2025)

Semidefinite Relaxations of the Gromov-Wasserstein Distance
by: Chen, Junyu, et al.
Published: (2023)

On the Error-Propagation of Inexact Hotelling's Deflation for Principal Component Analysis
by: Liao, Fangshuo, et al.
Published: (2023)

Wasserstein Distributionally Robust Shallow Convex Neural Networks
by: Pallage, Julien, et al.
Published: (2024)

On Generalization and Regularization via Wasserstein Distributionally Robust Optimization
by: Wu, Qinyu, et al.
Published: (2022)

Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
by: Kuhn, Daniel, et al.
Published: (2019)

Sliced Inner Product Gromov-Wasserstein Distances
by: Gong, Xiaoyun, et al.
Published: (2026)

Tight Robustness Certificates and Wasserstein Distributional Attacks for Deep Neural Networks
by: Le, Bach C., et al.
Published: (2025)

Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls
by: Selvi, Aras, et al.
Published: (2024)

A Novel Sliced Fused Gromov-Wasserstein Distance
by: Piening, Moritz, et al.
Published: (2025)

Wasserstein Distributionally Robust Nash Equilibrium Seeking with Heterogeneous Data: A Lagrangian Approach
by: Wang, Zifan, et al.
Published: (2025)

Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback
by: Wang, Yikai, et al.
Published: (2026)

Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching
by: Chemseddine, Jannis, et al.
Published: (2024)

Contextual Optimization under Covariate Shift: A Robust Approach by Intersecting Wasserstein Balls
by: Wang, Tianyu, et al.
Published: (2024)

Provable Model-Parallel Distributed Principal Component Analysis with Parallel Deflation
by: Liao, Fangshuo, et al.
Published: (2025)

Wasserstein Distributionally Robust Estimation in High Dimensions: Performance Analysis and Optimal Hyperparameter Tuning
by: Aolaritei, Liviu, et al.
Published: (2022)

Data-Driven Density Steering via the Gromov-Wasserstein Optimal Transport Distance
by: Nakashima, Haruto, et al.
Published: (2025)

Convergence Analysis of the Wasserstein Proximal Algorithm beyond Geodesic Convexity
by: Zhu, Shuailong, et al.
Published: (2025)

Byzantine-Robust Distributed SGD: A Unified Analysis and Tight Error Bounds
by: Ruan, Boyuan, et al.
Published: (2026)

Distributionally Robust Learning in Survival Analysis
by: Jin, Yeping, et al.
Published: (2025)

Data-driven Multistage Distributionally Robust Linear Optimization with Nested Distance
by: Gao, Rui, et al.
Published: (2024)

Wasserstein Distributionally Robust Risk-Sensitive Estimation via Conditional Value-at-Risk
by: Taha, Feras Al, et al.
Published: (2026)

A Provably Convergent and Practical Algorithm for Gromov--Wasserstein Optimal Transport
by: Liang, Ling, et al.
Published: (2026)

Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic
by: Zhang, Yufeng, et al.
Published: (2021)

Flowing Datasets with Wasserstein over Wasserstein Gradient Flows
by: Bonet, Clément, et al.
Published: (2025)

Data-driven Reachable Set Estimation with Tunable Adversarial and Wasserstein Distributional Guarantees
by: Pantazis, Georgios, et al.
Published: (2026)

A Short and General Duality Proof for Wasserstein Distributionally Robust Optimization
by: Zhang, Luhao, et al.
Published: (2022)

Sample Complexity of Distributionally Robust Average-Reward Reinforcement Learning
by: Chen, Zijun, et al.
Published: (2025)

Sinkhorn Distributionally Robust Optimization
by: Wang, Jie, et al.
Published: (2021)

A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints
by: Wang, Lei, et al.
Published: (2022)

Distributionally Robust Multi-Objective Optimization
by: Yang, Yufeng, et al.
Published: (2026)

On the Foundation of Distributionally Robust Reinforcement Learning
by: Wang, Shengbo, et al.
Published: (2023)

Distributionally Robust Optimization
by: Kuhn, Daniel, et al.
Published: (2024)

A Computational Framework for Solving Wasserstein Lagrangian Flows
by: Neklyudov, Kirill, et al.
Published: (2023)

Distributionally Robust Federated Learning with Outlier Resilience
by: Wang, Zifan, et al.
Published: (2025)

Linear Partial Gromov-Wasserstein Embedding
by: Bai, Yikun, et al.
Published: (2024)

Fréchet Regression on the Bures-Wasserstein Manifold
by: Nguyen, Duc Toan, et al.
Published: (2026)