6 citations · 8 across the 9 of their papers we have counts for
11 papers
Not All Change Is Damage: Update-Matched Unlearning for Large Language Models
Jie Li, Youming Tao, Bowei He +1
Unlearning aims to remove selected knowledge from large language models to mitigate privacy and safety risks while preserving useful capabilities. For models that continue to be fi…
Differentially Private Non-convex Distributionally Robust Optimization
Difei Xu, Meng Ding, Zebin Ma +4
Real-world deployments routinely face distribution shifts, group imbalances, and adversarial perturbations, under which the traditional Empirical Risk Minimization (ERM) framework…
Finding Differentially Private Second Order Stationary Points in Stochastic Minimax Optimization
Difei Xu, Youming Tao, Meng Ding +2
We provide the first study of the problem of finding differentially private (DP) second-order stationary points (SOSP) in stochastic (non-convex) minimax optimization. Existing lit…
Certified Unlearning in Decentralized Federated Learning
Hengliang Wu, Youming Tao, Anhao Zhou +3
Driven by the right to be forgotten (RTBF), machine unlearning has become an essential requirement for privacy-preserving machine learning. However, its realization in decentralize…
Second-Order Convergence in Private Stochastic Non-Convex Optimization
Youming Tao, Zuyuan Zhang, Dongxiao Yu +3
We investigate the problem of finding second-order stationary points (SOSP) in differentially private (DP) stochastic non-convex optimization. Existing methods suffer from two key…
Communication Efficient and Provable Federated Unlearning
Youming Tao, Cheng-Long Wang, Miao Pan +3
We study federated unlearning, a novel problem to eliminate the impact of specific clients or data points on the global model learned via federated learning (FL). This problem is d…
Adaptive pruning-based Newton's method for distributed learning
Shuzhen Chen, Yuan Yuan, Youming Tao +3
Newton's method leverages curvature information to boost performance, and thus outperforms first-order methods for distributed learning problems. However, Newton's method is not pr…
Byzantine-Resilient Federated Learning at Edge
Youming Tao, Sijia Cui, Wenlu Xu +4
Both Byzantine resilience and communication efficiency have attracted tremendous attention recently for their significance in edge federated learning. However, most existing algori…
On Private and Robust Bandits
Yulian Wu, Xingyu Zhou, Youming Tao +1
We study private and robust multi-armed bandits (MABs), where the agent receives Huber's contaminated heavy-tailed rewards and meanwhile needs to ensure differential privacy. We fi…
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits
Youming Tao, Yulian Wu, Peng Zhao +1
In this paper we investigate the problem of stochastic multi-armed bandits (MAB) in the (local) differential privacy (DP/LDP) model. Unlike previous results that assume bounded/sub…
A Distributed Privacy-Preserving Learning Dynamics in General Social Networks
Youming Tao, Shuzhen Chen, Feng Li +3
In this paper, we study a distributed privacy-preserving learning problem in social networks with general topology. The agents can communicate with each other over the network, whi…