On the Sample Complexity of Differentially Private Policy Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | He, Yi, Zhou, Xingyu |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Towards Differentially Private Reinforcement Learning with General Function Approximation
by: He, Yi, et al.
Published: (2026)
by: He, Yi, et al.
Published: (2026)
Improved Bounds for Private and Robust Alignment
by: Weng, Wenqian, et al.
Published: (2025)
by: Weng, Wenqian, et al.
Published: (2025)
DP-OPD: Differentially Private On-Policy Distillation for Language Models
by: Khadem, Fatemeh, et al.
Published: (2026)
by: Khadem, Fatemeh, et al.
Published: (2026)
A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO
by: Zhou, Xingyu, et al.
Published: (2025)
by: Zhou, Xingyu, et al.
Published: (2025)
Auditing Approximate Machine Unlearning for Differentially Private Models
by: Gu, Yuechun, et al.
Published: (2025)
by: Gu, Yuechun, et al.
Published: (2025)
Complexity-Regularized Proximal Policy Optimization
by: Serfilippi, Luca, et al.
Published: (2025)
by: Serfilippi, Luca, et al.
Published: (2025)
Multi-Objective Optimization for Privacy-Utility Balance in Differentially Private Federated Learning
by: Ranaweera, Kanishka, et al.
Published: (2025)
by: Ranaweera, Kanishka, et al.
Published: (2025)
ESPO: Entropy Importance Sampling Policy Optimization
by: Sheng, Yuepeng, et al.
Published: (2025)
by: Sheng, Yuepeng, et al.
Published: (2025)
GIPO: Gaussian Importance Sampling Policy Optimization
by: Lu, Chengxuan, et al.
Published: (2026)
by: Lu, Chengxuan, et al.
Published: (2026)
DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Models
by: Liu, Jin, et al.
Published: (2026)
by: Liu, Jin, et al.
Published: (2026)
Hybrid Group Relative Policy Optimization: A Multi-Sample Approach to Enhancing Policy Optimization
by: Sane, Soham
Published: (2025)
by: Sane, Soham
Published: (2025)
Differentially Private Worst-group Risk Minimization
by: Zhou, Xinyu, et al.
Published: (2024)
by: Zhou, Xinyu, et al.
Published: (2024)
Beyond Importance Sampling: Rejection-Gated Policy Optimization
by: Sun, Ziwu, et al.
Published: (2026)
by: Sun, Ziwu, et al.
Published: (2026)
DP-CSGP: Differentially Private Stochastic Gradient Push with Compressed Communication
by: Zhu, Zehan, et al.
Published: (2025)
by: Zhu, Zehan, et al.
Published: (2025)
Differentially Private Federated Clustering with Random Rebalancing
by: Yang, Xiyuan, et al.
Published: (2025)
by: Yang, Xiyuan, et al.
Published: (2025)
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning
by: Narang, Adhyyan, et al.
Published: (2024)
by: Narang, Adhyyan, et al.
Published: (2024)
KL-regularization Itself is Differentially Private in Bandits and RLHF
by: Zhang, Yizhou, et al.
Published: (2025)
by: Zhang, Yizhou, et al.
Published: (2025)
Certification for Differentially Private Prediction in Gradient-Based Training
by: Wicker, Matthew, et al.
Published: (2024)
by: Wicker, Matthew, et al.
Published: (2024)
Differentiable Entropy Regularization: A Complexity-Aware Approach for Neural Optimization
by: Shihab, Ibne Farabi, et al.
Published: (2025)
by: Shihab, Ibne Farabi, et al.
Published: (2025)
Differentially Private Model Merging
by: Yin, Qichuan, et al.
Published: (2026)
by: Yin, Qichuan, et al.
Published: (2026)
Relative Policy-Transition Optimization for Fast Policy Transfer
by: Xu, Jiawei, et al.
Published: (2022)
by: Xu, Jiawei, et al.
Published: (2022)
Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization
by: Liu, Zongkai, et al.
Published: (2024)
by: Liu, Zongkai, et al.
Published: (2024)
Rethinking Importance Sampling in LLM Policy Optimization: A Cumulative Token Perspective
by: Zhang, Yuheng, et al.
Published: (2026)
by: Zhang, Yuheng, et al.
Published: (2026)
Unifying Group-Relative and Self-Distillation Policy Optimization via Sample Routing
by: Li, Gengsheng, et al.
Published: (2026)
by: Li, Gengsheng, et al.
Published: (2026)
Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
by: Gao, Fengyu, et al.
Published: (2024)
by: Gao, Fengyu, et al.
Published: (2024)
Decision Flow Policy Optimization
by: Hu, Jifeng, et al.
Published: (2025)
by: Hu, Jifeng, et al.
Published: (2025)
Understanding the Impact of Differentially Private Training on Memorization of Long-Tailed Data
by: Zhang, Jiaming, et al.
Published: (2026)
by: Zhang, Jiaming, et al.
Published: (2026)
Generalizing Differentially Private Decentralized Deep Learning with Multi-Agent Consensus
by: Bayrooti, Jasmine, et al.
Published: (2023)
by: Bayrooti, Jasmine, et al.
Published: (2023)
Demystifying the Paradox of Importance Sampling with an Estimated History-Dependent Behavior Policy in Off-Policy Evaluation
by: Zhou, Hongyi, et al.
Published: (2025)
by: Zhou, Hongyi, et al.
Published: (2025)
On-Policy Optimization of ANFIS Policies Using Proximal Policy Optimization
by: Shankar, Kaaustaaub, et al.
Published: (2025)
by: Shankar, Kaaustaaub, et al.
Published: (2025)
Unearthing Gems from Stones: Policy Optimization with Negative Sample Augmentation for LLM Reasoning
by: Yang, Zhaohui, et al.
Published: (2025)
by: Yang, Zhaohui, et al.
Published: (2025)
RLVR without Ineffective Samples: Group Prioritized Off-Policy Optimization for LLM Reasoning
by: Mao, Yixiu, et al.
Published: (2026)
by: Mao, Yixiu, et al.
Published: (2026)
Reconstruction of Differentially Private Text Sanitization via Large Language Models
by: Pang, Shuchao, et al.
Published: (2024)
by: Pang, Shuchao, et al.
Published: (2024)
DP-FedPGN: Finding Global Flat Minima for Differentially Private Federated Learning via Penalizing Gradient Norm
by: Liu, Junkang, et al.
Published: (2025)
by: Liu, Junkang, et al.
Published: (2025)
Sample from What You See: Visuomotor Policy Learning via Diffusion Bridge with Observation-Embedded Stochastic Differential Equation
by: Liu, Zhaoyang, et al.
Published: (2025)
by: Liu, Zhaoyang, et al.
Published: (2025)
Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning
by: Auddy, Arnab, et al.
Published: (2026)
by: Auddy, Arnab, et al.
Published: (2026)
Learning with Differentially Private (Sliced) Wasserstein Gradients
by: Rodríguez-Vítores, David, et al.
Published: (2025)
by: Rodríguez-Vítores, David, et al.
Published: (2025)
SLOT: Sample-specific Language Model Optimization at Test-time
by: Hu, Yang, et al.
Published: (2025)
by: Hu, Yang, et al.
Published: (2025)
LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch
by: Jiang, Caigao, et al.
Published: (2024)
by: Jiang, Caigao, et al.
Published: (2024)
CROP: Conservative Reward for Model-based Offline Policy Optimization
by: Li, Hao, et al.
Published: (2023)
by: Li, Hao, et al.
Published: (2023)
Similar Items
-
Towards Differentially Private Reinforcement Learning with General Function Approximation
by: He, Yi, et al.
Published: (2026) -
Improved Bounds for Private and Robust Alignment
by: Weng, Wenqian, et al.
Published: (2025) -
DP-OPD: Differentially Private On-Policy Distillation for Language Models
by: Khadem, Fatemeh, et al.
Published: (2026) -
A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO
by: Zhou, Xingyu, et al.
Published: (2025) -
Auditing Approximate Machine Unlearning for Differentially Private Models
by: Gu, Yuechun, et al.
Published: (2025)