Cover Image

PrivORL: Differentially Private Synthetic Dataset for Offline Reinforcement Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gong, Chen, Liu, Zheng, Li, Kecen, Wang, Tianhao
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

PrivImage: Differentially Private Synthetic Image Generation using Diffusion Models with Semantic-Aware Pretraining
by: Li, Kecen, et al.
Published: (2023)

TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning Agents
by: Gong, Chen, et al.
Published: (2024)

BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets
by: Gong, Chen, et al.
Published: (2022)

PrivCode: When Code Generation Meets Differential Privacy
by: Liu, Zheng, et al.
Published: (2025)

Differentially Private Contrastive Learning via Bounding Group-level Contribution
by: Li, Kecen, et al.
Published: (2026)

DP-SAPF: Saliency-Aware Parameter Fine-tuning of Public Models for Differentially Private Image Synthesis
by: Gong, Chen, et al.
Published: (2026)

Beyond One-Size-Fits-All: Neural Networks for Differentially Private Tabular Data Synthesis
by: Chen, Kai, et al.
Published: (2025)

DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis
by: Gong, Chen, et al.
Published: (2025)

PrivATE: Differentially Private Confidence Intervals for Average Treatment Effects
by: Schröder, Maresa, et al.
Published: (2025)

Revisiting Differentially Private Hyper-parameter Tuning
by: Xiang, Zihang, et al.
Published: (2024)

From Easy to Hard++: Promoting Differentially Private Image Synthesis Through Spatial-Frequency Curriculum
by: Gong, Chen, et al.
Published: (2026)

From Easy to Hard: Building a Shortcut for Differentially Private Image Synthesis
by: Li, Kecen, et al.
Published: (2025)

Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
by: Gao, Fengyu, et al.
Published: (2024)

DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning
by: Liu, Yixuan, et al.
Published: (2024)

Inference With Combining Rules From Multiple Differentially Private Synthetic Datasets
by: Nombo, Leila, et al.
Published: (2024)

Differentially Private Synthetic Data Generation for Relational Databases
by: Alimohammadi, Kaveh, et al.
Published: (2024)

Dual-Priv Pruning : Efficient Differential Private Fine-Tuning in Multimodal Large Language Models
by: Wei, Qianshan, et al.
Published: (2025)

ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations
by: Ling, Xinpeng, et al.
Published: (2023)

LanFL: Differentially Private Federated Learning with Large Language Models using Synthetic Samples
by: Wu, Huiyu, et al.
Published: (2024)

Differentially Private Federated Learning: A Systematic Review
by: Fu, Jie, et al.
Published: (2024)

Privately Learning Decision Lists and a Differentially Private Winnow
by: Bun, Mark, et al.
Published: (2026)

PrivGNN: High-Performance Secure Inference for Cryptographic Graph Neural Networks
by: Wang, Fuyi, et al.
Published: (2025)

Differentially Private Learned Indexes
by: Du, Jianzhang, et al.
Published: (2024)

Calibrating Practical Privacy Risks for Differentially Private Machine Learning
by: Gu, Yuechun, et al.
Published: (2024)

Differentially Private Range Queries with Correlated Input Perturbation
by: Dharangutte, Prathamesh, et al.
Published: (2024)

Differentially Private Deep Model-Based Reinforcement Learning
by: Rio, Alexandre, et al.
Published: (2024)

Private and Communication-Efficient Federated Learning based on Differentially Private Sketches
by: Zhang, Meifan, et al.
Published: (2024)

Correlated Noise Mechanisms for Differentially Private Learning
by: Pillutla, Krishna, et al.
Published: (2025)

Differentially Private Release and Learning of Threshold Functions
by: Bun, Mark, et al.
Published: (2015)

Differentially Private Decentralized Learning with Random Walks
by: Cyffers, Edwige, et al.
Published: (2024)

The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data
by: Ganev, Georgi, et al.
Published: (2025)

Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
by: Zhou, Yvonne, et al.
Published: (2024)

Differentially Private Relational Learning with Entity-level Privacy Guarantees
by: Huang, Yinan, et al.
Published: (2025)

Preserving Expert-Level Privacy in Offline Reinforcement Learning
by: Sharma, Navodita, et al.
Published: (2024)

Delving into Differentially Private Transformer
by: Ding, Youlong, et al.
Published: (2024)

Differentially Private Reinforcement Learning with Self-Play
by: Qiao, Dan, et al.
Published: (2024)

Differentially Private Subspace Fine-Tuning for Large Language Models
by: Zheng, Lele, et al.
Published: (2026)

Efficient Differentially Private Fine-Tuning of Diffusion Models
by: Liu, Jing, et al.
Published: (2024)

Oracle-Efficient Differentially Private Learning with Public Data
by: Block, Adam, et al.
Published: (2024)

Privately Aligning Language Models with Reinforcement Learning
by: Wu, Fan, et al.
Published: (2023)