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20222026
most citedUnderstanding Forgetting in Continual Learning with Linear Regression

1 citations · 2 across the 14 of their papers we have counts for

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cs.CR2026

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…

cs.LG2026

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…

cs.LG2026

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,…

cs.LG2026

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…

cs.LG2026

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…