Imagen de Portada

Private Wasserstein Distance

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Li, Wenqian, Pang, Yan
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Distance-Based Tree-Sliced Wasserstein Distance
por: Tran, Hoang V., et al.
Publicado: (2025)

Improved Bounds for Private and Robust Alignment
por: Weng, Wenqian, et al.
Publicado: (2025)

Data Valuation and Selection in a Federated Model Marketplace
por: Li, Wenqian, et al.
Publicado: (2025)

Wasserstein Distances, Neuronal Entanglement, and Sparsity
por: Sawmya, Shashata, et al.
Publicado: (2024)

Spherical Tree-Sliced Wasserstein Distance
por: Tran, Viet-Hoang, et al.
Publicado: (2025)

Tree-Sliced Wasserstein Distance with Nonlinear Projection
por: Tran, Thanh, et al.
Publicado: (2025)

Data Valuation and Detections in Federated Learning
por: Li, Wenqian, et al.
Publicado: (2023)

Tree-Sliced Wasserstein Distance: A Geometric Perspective
por: Tran, Viet-Hoang, et al.
Publicado: (2024)

Measuring Time-Series Dataset Similarity using Wasserstein Distance
por: Chen, Hongjie, et al.
Publicado: (2025)

Learning with Differentially Private (Sliced) Wasserstein Gradients
por: Rodríguez-Vítores, David, et al.
Publicado: (2025)

Stereographic Spherical Sliced Wasserstein Distances
por: Tran, Huy, et al.
Publicado: (2024)

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation
por: Lv, Jiaming, et al.
Publicado: (2024)

Intelligent Icing Detection Model of Wind Turbine Blades Based on SCADA data
por: Jiang, Wenqian, et al.
Publicado: (2021)

Canonical Variates in Wasserstein Metric Space
por: Li, Jia, et al.
Publicado: (2024)

On Differentially Private String Distances
por: Hu, Jerry Yao-Chieh, et al.
Publicado: (2024)

Wasserstein Policy Optimization
por: Pfau, David, et al.
Publicado: (2025)

Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching
por: Yan, Kai, et al.
Publicado: (2023)

On the Wasserstein Gradient Flow Interpretation of Drifting Models
por: Gretton, Arthur, et al.
Publicado: (2026)

Rectifying Shortcut Behaviors in Preference-based Reward Learning
por: Ye, Wenqian, et al.
Publicado: (2025)

Wasserstein Distances Made Explainable: Insights Into Dataset Shifts and Transport Phenomena
por: Naumann, Philip, et al.
Publicado: (2025)

Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks
por: Chen, Wenqian, et al.
Publicado: (2024)

Graph data augmentation with Gromow-Wasserstein Barycenters
por: Ponti, Andrea
Publicado: (2024)

Downscaling Extreme Precipitation with Wasserstein Regularized Diffusion
por: Liu, Yuhao, et al.
Publicado: (2024)

Fourier Sliced-Wasserstein Embedding for Multisets and Measures
por: Amir, Tal, et al.
Publicado: (2025)

Bures-Wasserstein Flow Matching for Graph Generation
por: Jiang, Keyue, et al.
Publicado: (2025)

DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment
por: Wang, Hao, et al.
Publicado: (2025)

Wasserstein Distributionally Robust Bayesian Optimization with Continuous Context
por: Micheli, Francesco, et al.
Publicado: (2025)

Wasserstein Gradient Flows for Scalable and Regularized Barycenter Computation
por: Montesuma, Eduardo Fernandes, et al.
Publicado: (2025)

Differentially Private Federated Clustering with Random Rebalancing
por: Yang, Xiyuan, et al.
Publicado: (2025)

Computing Approximate Graph Edit Distance via Optimal Transport
por: Cheng, Qihao, et al.
Publicado: (2024)

DP-CSGP: Differentially Private Stochastic Gradient Push with Compressed Communication
por: Zhu, Zehan, et al.
Publicado: (2025)

From Private to Public: Benchmarking GANs in the Context of Private Time Series Classification
por: Mercier, Dominique, et al.
Publicado: (2023)

Reconstruction of Differentially Private Text Sanitization via Large Language Models
por: Pang, Shuchao, et al.
Publicado: (2024)

A Fused Gromov-Wasserstein Approach to Subgraph Contrastive Learning
por: Sangare, Amadou S., et al.
Publicado: (2025)

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee
por: Zhu, Zehan, et al.
Publicado: (2025)

Is Distance Matrix Enough for Geometric Deep Learning?
por: Li, Zian, et al.
Publicado: (2023)

Revisiting Counterfactual Regression through the Lens of Gromov-Wasserstein Information Bottleneck
por: Yang, Hao, et al.
Publicado: (2024)

Multi-scale Wasserstein Shortest-path Graph Kernels for Graph Classification
por: Ye, Wei, et al.
Publicado: (2022)

Semantic-aware Wasserstein Policy Regularization for Large Language Model Alignment
por: Na, Byeonghu, et al.
Publicado: (2026)

A Decomposition Modeling Framework for Seasonal Time-Series Forecasting
por: Pang, Yining, et al.
Publicado: (2024)