96 citations · 103 across the 8 of their papers we have counts for
6 papers · 1 filter
Differentially Private Reinforcement Learning with Self-Play
Dan Qiao, Yu-Xiang Wang
We study the problem of multi-agent reinforcement learning (multi-agent RL) with differential privacy (DP) constraints. This is well-motivated by various real-world applications in…
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
Rachel Redberg, Antti Koskela, Yu-Xiang Wang
In the arena of privacy-preserving machine learning, differentially private stochastic gradient descent (DP-SGD) has outstripped the objective perturbation mechanism in popularity…
Coupling public and private gradient provably helps optimization
Ruixuan Liu, Zhiqi Bu, Yu-xiang Wang +2
The success of large neural networks is crucially determined by the availability of data. It has been observed that training only on a small amount of public data, or privately on…
Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation
Jiachen T. Wang, Yuqing Zhu, Yu-Xiang Wang +2
Data valuation aims to quantify the usefulness of individual data sources in training machine learning (ML) models, and is a critical aspect of data-centric ML research. However, d…
"Private Prediction Strikes Back!'' Private Kernelized Nearest Neighbors with Individual Renyi Filter
Yuqing Zhu, Xuandong Zhao, Chuan Guo +1
Most existing approaches of differentially private (DP) machine learning focus on private training. Despite its many advantages, private training lacks the flexibility in adapting…
Non-stationary Reinforcement Learning under General Function Approximation
Songtao Feng, Ming Yin, Ruiquan Huang +3
General function approximation is a powerful tool to handle large state and action spaces in a broad range of reinforcement learning (RL) scenarios. However, theoretical understand…