:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Šiška, David, Zhang, Yufei
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Optimization and Control
Online Access:	https://arxiv.org/abs/2605.22622
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Linear convergence of proximal descent schemes on the Wasserstein space
by: Lascu, Razvan-Andrei, et al.
Published: (2024)

Entropy annealing for policy mirror descent in continuous time and space
by: Sethi, Deven, et al.
Published: (2024)

PPO in the Fisher-Rao geometry
by: Lascu, Razvan-Andrei, et al.
Published: (2025)

Non-geodesically-convex optimization in the Wasserstein space
by: Luu, Hoang Phuc Hau, et al.
Published: (2024)

Worst-case generation via minimax optimization in Wasserstein space
by: Cheng, Xiuyuan, et al.
Published: (2025)

Unregularized limit of stochastic gradient method for Wasserstein distributionally robust optimization
by: Le, Tam
Published: (2025)

Gromov-Wasserstein and optimal transport: from assignment problems to probabilistic numeric
by: Seyedi, Iman, et al.
Published: (2025)

Learning to optimize with guarantees: a complete characterization of linearly convergent algorithms
by: Martin, Andrea, et al.
Published: (2025)

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

A stochastic smoothing framework for nonconvex-nonconcave min-sum-max problems with applications to Wasserstein distributionally robust optimization
by: Liu, Wei, et al.
Published: (2025)

Mirror and Preconditioned Gradient Descent in Wasserstein Space
by: Bonet, Clément, et al.
Published: (2024)

Mirror descent actor-critic methods for entropy regularised MDPs in general spaces: stability and convergence
by: Zorba, Denis, et al.
Published: (2026)

Collaborative Bayesian Optimization via Wasserstein Barycenters
by: Zhan, Donglin, et al.
Published: (2025)

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

Gradient Flows for Regularized Stochastic Control Problems
by: Šiška, David, et al.
Published: (2020)

Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space
by: Jiang, Yiheng, et al.
Published: (2023)

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

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

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

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

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

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

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

Sliced Wasserstein Steering between Gaussian Measures
by: Ito, Kaito, et al.
Published: (2026)

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

Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks
by: Jentzen, Arnulf, et al.
Published: (2024)

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

Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms
by: Mohammadi, Hesameddin, et al.
Published: (2022)

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

Conditional Sampling via Wasserstein Autoencoders and Triangular Transport
by: Al-Jarrah, Mohammad, et al.
Published: (2026)

Random Coordinate Descent on the Wasserstein Space of Probability Measures
by: Xu, Yewei, et al.
Published: (2026)

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

Wasserstein Barycenter Gaussian Process based Bayesian Optimization
by: Candelieri, Antonio, et al.
Published: (2025)

It's All in the Mix: Wasserstein Classification and Regression with Mixed Features
by: Belbasi, Reza, et al.
Published: (2023)

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

Universal generalization guarantees for Wasserstein distributionally robust models
by: Le, Tam, et al.
Published: (2024)

Slicing Wasserstein Over Wasserstein Via Functional Optimal Transport
by: Piening, Moritz, et al.
Published: (2025)

Inclusive KL Minimization: A Wasserstein-Fisher-Rao Gradient Flow Perspective
by: Zhu, Jia-Jie
Published: (2024)

Local convergence of simultaneous min-max algorithms to differential equilibrium on Riemannian manifold
by: Zhang, Sixin
Published: (2024)

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