Cover Image

Distributionally Fair Stochastic Optimization using Wasserstein Distance

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ye, Qing, Hanasusanto, Grani A., Xie, Weijun
Format:	Preprint
Published:	2024
Subjects:	Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Generalization Bounds for Contextual Stochastic Optimization using Kernel Regression
by: Wang, Yijie, et al.
Published: (2024)

Sample Complexity of Data-driven Multistage Stochastic Programming under Markovian Uncertainty
by: Park, Hyuk, et al.
Published: (2024)

Distributionally Robust Optimization with Decision-Dependent Information Discovery
by: Jin, Qing, et al.
Published: (2024)

Distributionally Robust Performative Optimization
by: Jia, Zhuangzhuang, et al.
Published: (2024)

A Distributionally Robust Optimization Approach to Quick Response Models under Demand Uncertainty
by: Papavassilopoulos, Panayotis P., et al.
Published: (2025)

Learning Fair Policies for Infectious Diseases Mitigation using Path Integral Control
by: Jia, Zhuangzhuang, et al.
Published: (2025)

Data-Driven Contextual Optimization with Gaussian Mixtures: Flow-Based Generalization, Robust Models, and Multistage Extensions
by: Yoon, YoungChul, et al.
Published: (2025)

Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes
by: Zhou, Duo, et al.
Published: (2024)

Closing the Gap: Efficient Algorithms for Discrete Wasserstein Barycenters
by: Wang, Jiaqi, et al.
Published: (2025)

DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
by: Cui, Mingxuan, et al.
Published: (2025)

On Tractability, Complexity, and Mixed-Integer Convex Programming Representability of Distributionally Favorable Optimization
by: Jiang, Nan, et al.
Published: (2024)

Clip-and-Verify: Linear Constraint-Driven Domain Clipping for Accelerating Neural Network Verification
by: Zhou, Duo, et al.
Published: (2025)

On the ReLU Lagrangian Cuts for Stochastic Mixed Integer Programming
by: Deng, Haoyun, et al.
Published: (2024)

Efficient Uncertainty Propagation with Guarantees in Wasserstein Distance
by: Figueiredo, Eduardo, et al.
Published: (2025)

Enhancing Distributional Robustness in Principal Component Analysis by Wasserstein Distances
by: Wang, Lei, et al.
Published: (2025)

Compressed Distributed Stochastic Nonconvex Optimization with Differential Privacy
by: Xie, Antai, et al.
Published: (2026)

Moment Relaxations for Data-Driven Wasserstein Distributionally Robust Optimization
by: Zhang, Shixuan, et al.
Published: (2025)

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

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

Stochastic Non-Smooth Non-Convex Optimization with Decision-Dependent Distributions
by: Liu, Chengchang, et al.
Published: (2026)

A Communication-Efficient Stochastic Gradient Descent Algorithm for Distributed Nonconvex Optimization
by: Xie, Antai, et al.
Published: (2024)

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

Wasserstein Distributionally Robust Performative Prediction
by: Wang, Siyi, et al.
Published: (2026)

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

The Wasserstein Space of Stochastic Processes in Continuous Time
by: Bartl, Daniel, et al.
Published: (2025)

Compressed Momentum-based Single-Point Zeroth-Order Algorithm for Stochastic Distributed Nonconvex Optimization
by: Chen, Linjing, et al.
Published: (2025)

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

Entropic Gromov-Wasserstein Distances: Stability and Algorithms
by: Rioux, Gabriel, et al.
Published: (2023)

Trust Region Methods For Nonconvex Stochastic Optimization Beyond Lipschitz Smoothness
by: Xie, Chenghan, et al.
Published: (2023)

Distance Between Stochastic Linear Systems
by: Renganathan, Venkatraman, et al.
Published: (2025)

Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity
by: Shi, Qiankun, et al.
Published: (2025)

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

Distributed Stochastic Optimization under Heavy-Tailed Noises
by: Sun, Chao, et al.
Published: (2023)

Distributionally Robust Density Control with Wasserstein Ambiguity Sets
by: Pilipovsky, Joshua, et al.
Published: (2024)

Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis
by: Niu, Youcheng, et al.
Published: (2023)

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

Wasserstein Distributionally Robust Optimization with Heterogeneous Data Sources
by: Rychener, Yves, et al.
Published: (2024)

Optimal Transport and Wasserstein Barycenter for Radially Contoured Distributions
by: Chen, Keyu, et al.
Published: (2024)

First-order Conditions for Optimization in the Wasserstein Space
by: Lanzetti, Nicolas, et al.
Published: (2022)

Generalized Dimension Reduction Using Semi-Relaxed Gromov-Wasserstein Distance
by: Clark, Ranthony A., et al.
Published: (2024)