Differentially Private Wasserstein Barycenters
Fuente:
arXiv
Saved in:
| Main Authors: | Gu, Anming, Kunapuli, Sasidhar, Bun, Mark, Chien, Edward, Greenewald, Kristjan |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamics
by: Gu, Anming, et al.
Published: (2025)
by: Gu, Anming, et al.
Published: (2025)
Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior
by: Gu, Anming, et al.
Published: (2024)
by: Gu, Anming, et al.
Published: (2024)
Privately Learning Decision Lists and a Differentially Private Winnow
by: Bun, Mark, et al.
Published: (2026)
by: Bun, Mark, et al.
Published: (2026)
Continuous Symmetry Discovery and Enforcement Using Infinitesimal Generators of Multi-parameter Group Actions
by: Shaw, Ben, et al.
Published: (2025)
by: Shaw, Ben, et al.
Published: (2025)
Gradient Flows and Riemannian Structure in the Gromov-Wasserstein Geometry
by: Zhang, Zhengxin, et al.
Published: (2024)
by: Zhang, Zhengxin, et al.
Published: (2024)
Differentially Private Release and Learning of Threshold Functions
by: Bun, Mark, et al.
Published: (2015)
by: Bun, Mark, et al.
Published: (2015)
Distributional Process Reward Models: Calibrated Prediction of Future Rewards via Conditional Optimal Transport
by: Ma, Rachel, et al.
Published: (2026)
by: Ma, Rachel, et al.
Published: (2026)
Improved Sample Complexity For Diffusion Model Training Without Empirical Risk Minimizer Access
by: Gaur, Mudit, et al.
Published: (2025)
by: Gaur, Mudit, et al.
Published: (2025)
Neural Estimation for Scaling Entropic Multimarginal Optimal Transport
by: Tsur, Dor, et al.
Published: (2025)
by: Tsur, Dor, et al.
Published: (2025)
Oracle-Efficient Differentially Private Learning with Public Data
by: Block, Adam, et al.
Published: (2024)
by: Block, Adam, et al.
Published: (2024)
Wasserstein Barycenter Soft Actor-Critic
by: Shahrooei, Zahra, et al.
Published: (2025)
by: Shahrooei, Zahra, et al.
Published: (2025)
Towards Marginal Fairness Sliced Wasserstein Barycenter
by: Nguyen, Khai, et al.
Published: (2024)
by: Nguyen, Khai, et al.
Published: (2024)
Entropic Causal Inference: Graph Identifiability
by: Compton, Spencer, et al.
Published: (2025)
by: Compton, Spencer, et al.
Published: (2025)
Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training
by: Greenewald, Kristjan, et al.
Published: (2024)
by: Greenewald, Kristjan, et al.
Published: (2024)
Slicing Mutual Information Generalization Bounds for Neural Networks
by: Nadjahi, Kimia, et al.
Published: (2024)
by: Nadjahi, Kimia, et al.
Published: (2024)
Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
by: Wei, Ting, et al.
Published: (2025)
by: Wei, Ting, et al.
Published: (2025)
Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters
by: Pereira, Luiz, et al.
Published: (2025)
by: Pereira, Luiz, et al.
Published: (2025)
Approximate Algorithms For $k$-Sparse Wasserstein Barycenter With Outliers
by: Yang, Qingyuan, et al.
Published: (2024)
by: Yang, Qingyuan, et al.
Published: (2024)
Mirror Mean-Field Langevin Dynamics
by: Gu, Anming, et al.
Published: (2025)
by: Gu, Anming, et al.
Published: (2025)
Collaborative Bayesian Optimization via Wasserstein Barycenters
by: Zhan, Donglin, et al.
Published: (2025)
by: Zhan, Donglin, et al.
Published: (2025)
Graph data augmentation with Gromow-Wasserstein Barycenters
by: Ponti, Andrea
Published: (2024)
by: Ponti, Andrea
Published: (2024)
Linearized Wasserstein Barycenters: Synthesis, Analysis, Representational Capacity, and Applications
by: Werenski, Matthew, et al.
Published: (2024)
by: Werenski, Matthew, et al.
Published: (2024)
Wasserstein Barycenter Gaussian Process based Bayesian Optimization
by: Candelieri, Antonio, et al.
Published: (2025)
by: Candelieri, Antonio, et al.
Published: (2025)
Wasserstein Gradient Flows for Scalable and Regularized Barycenter Computation
by: Montesuma, Eduardo Fernandes, et al.
Published: (2025)
by: Montesuma, Eduardo Fernandes, et al.
Published: (2025)
Fairness in Multi-Task Learning via Wasserstein Barycenters
by: Hu, François, et al.
Published: (2023)
by: Hu, François, et al.
Published: (2023)
A Particle-Flow Algorithm for Free-Support Wasserstein Barycenters
by: You, Kisung
Published: (2025)
by: You, Kisung
Published: (2025)
Efficient Multi-Adapter LLM Serving via Cross-Model KV-Cache Reuse with Activated LoRA
by: Li, Allison, et al.
Published: (2025)
by: Li, Allison, et al.
Published: (2025)
Weighted Wasserstein Barycenter of Gaussian Processes for exotic Bayesian Optimization tasks
by: Candelieri, Antonio, et al.
Published: (2026)
by: Candelieri, Antonio, et al.
Published: (2026)
Multivariate Stochastic Dominance via Optimal Transport and Applications to Models Benchmarking
by: Rioux, Gabriel, et al.
Published: (2024)
by: Rioux, Gabriel, et al.
Published: (2024)
MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection and Classification
by: Ige, Tosin, et al.
Published: (2025)
by: Ige, Tosin, et al.
Published: (2025)
Private PAC Learning May be Harder than Online Learning
by: Bun, Mark, et al.
Published: (2024)
by: Bun, Mark, et al.
Published: (2024)
Know What You Don't Know: Uncertainty Calibration of Process Reward Models
by: Park, Young-Jin, et al.
Published: (2025)
by: Park, Young-Jin, et al.
Published: (2025)
Doubly Regularized Entropic Wasserstein Barycenters
by: Chizat, Lénaïc
Published: (2023)
by: Chizat, Lénaïc
Published: (2023)
Learning with Differentially Private (Sliced) Wasserstein Gradients
by: Rodríguez-Vítores, David, et al.
Published: (2025)
by: Rodríguez-Vítores, David, et al.
Published: (2025)
Not All Learnable Distribution Classes are Privately Learnable
by: Bun, Mark, et al.
Published: (2024)
by: Bun, Mark, et al.
Published: (2024)
Thermometer: Towards Universal Calibration for Large Language Models
by: Shen, Maohao, et al.
Published: (2024)
by: Shen, Maohao, et al.
Published: (2024)
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance
by: Sebag, Ilana, et al.
Published: (2023)
by: Sebag, Ilana, et al.
Published: (2023)
Differentially Private Sampling from Distributions via Wasserstein Projection
by: Takakura, Shokichi, et al.
Published: (2026)
by: Takakura, Shokichi, et al.
Published: (2026)
Functional Stochastic Localization
by: Gu, Anming, et al.
Published: (2026)
by: Gu, Anming, et al.
Published: (2026)
Private Wasserstein Distance
by: Li, Wenqian, et al.
Published: (2024)
by: Li, Wenqian, et al.
Published: (2024)
Similar Items
-
Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamics
by: Gu, Anming, et al.
Published: (2025) -
Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior
by: Gu, Anming, et al.
Published: (2024) -
Privately Learning Decision Lists and a Differentially Private Winnow
by: Bun, Mark, et al.
Published: (2026) -
Continuous Symmetry Discovery and Enforcement Using Infinitesimal Generators of Multi-parameter Group Actions
by: Shaw, Ben, et al.
Published: (2025) -
Gradient Flows and Riemannian Structure in the Gromov-Wasserstein Geometry
by: Zhang, Zhengxin, et al.
Published: (2024)