1 citations · 2 across the 14 of their papers we have counts for
16 papers
Can Agents Keep Secrets They Were Never Told? Benchmarking Latent Privacy Risks Across Sessions with MemJoin
Qishun Yang, Shu Yang, Difei Xu +2
To support sustained interactions and personalized assistance, agents use long-term memory to retain and combine user information across sessions. Reusing such memories can lead an…
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…
Benign Overfitting in Adversarial Training for Vision Transformers
Jiaming Zhang, Meng Ding, Shaopeng Fu +2
Despite the remarkable success of Vision Transformers (ViTs) across a wide range of vision tasks, recent studies have revealed that they remain vulnerable to adversarial examples,…
Provable Effects of Data Replay in Continual Learning: A Feature Learning Perspective
Meng Ding, Jinhui Xu, Kaiyi Ji
Continual learning (CL) aims to train models on a sequence of tasks while retaining performance on previously learned ones. A core challenge in this setting is catastrophic forgett…
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…
Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition
Difei Xu, Meng Ding, Zihang Xiang +2
We study Stochastic Convex Optimization in the Differential Privacy model (DP-SCO). Unlike previous studies, here we assume the population risk function satisfies the Tsybakov Nois…
Understanding and Mitigating Cross-lingual Privacy Leakage via Language-specific and Universal Privacy Neurons
Wenshuo Dong, Qingsong Yang, Shu Yang +5
Large Language Models (LLMs) trained on massive data capture rich information embedded in the training data. However, this also introduces the risk of privacy leakage, particularly…
Differentially Private Sparse Linear Regression with Heavy-tailed Responses
Xizhi Tian, Meng Ding, Touming Tao +2
As a fundamental problem in machine learning and differential privacy (DP), DP linear regression has been extensively studied. However, most existing methods focus primarily on eit…
Improved Rates of Differentially Private Nonconvex-Strongly-Concave Minimax Optimization
Ruijia Zhang, Mingxi Lei, Meng Ding +3
In this paper, we study the problem of (finite sum) minimax optimization in the Differential Privacy (DP) model. Unlike most of the previous studies on the (strongly) convex-concav…
Nearly Optimal Differentially Private ReLU Regression
Meng Ding, Mingxi Lei, Shaowei Wang +3
In this paper, we investigate one of the most fundamental nonconvex learning problems, ReLU regression, in the Differential Privacy (DP) model. Previous studies on private ReLU reg…
Evaluating Data Influence in Meta Learning
Chenyang Ren, Huanyi Xie, Shu Yang +3
As one of the most fundamental models, meta learning aims to effectively address few-shot learning challenges. However, it still faces significant issues related to the training da…
Truthful High Dimensional Sparse Linear Regression
Liyang Zhu, Amina Manseur, Meng Ding +3
We study the problem of fitting the high dimensional sparse linear regression model with sub-Gaussian covariates and responses, where the data are provided by strategic or self-int…
Understanding Fine-tuning in Approximate Unlearning: A Theoretical Perspective
Meng Ding, Rohan Sharma, Changyou Chen +2
Machine Unlearning has emerged as a significant area of research, focusing on `removing' specific subsets of data from a trained model. Fine-tuning (FT) methods have become one of…
Understanding Forgetting in Continual Learning with Linear Regression
Meng Ding, Kaiyi Ji, Di Wang +1
Continual learning, focused on sequentially learning multiple tasks, has gained significant attention recently. Despite the tremendous progress made in the past, the theoretical un…
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model
Liyang Zhu, Meng Ding, Vaneet Aggarwal +2
In this paper, we revisit the problem of sparse linear regression in the local differential privacy (LDP) model. Existing research in the non-interactive and sequentially local mod…
On Stability and Generalization of Bilevel Optimization Problem
Meng Ding, Mingxi Lei, Yunwen Lei +2
(Stochastic) bilevel optimization is a frequently encountered problem in machine learning with a wide range of applications such as meta-learning, hyper-parameter optimization, and…