26 citations · 51 across the 17 of their papers we have counts for
9 papers · 1 filter
Generalizable Multimodal Large Language Model Editing via Invariant Trajectory Learning
Jiajie Su, Haoyuan Wang, Xiaohua Feng +6
Knowledge editing emerges as a crucial technique for efficiently correcting incorrect or outdated knowledge in large language models (LLM). Existing editing methods rely on a rigid…
FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning
Li Zhang, Zhongxuan Han, Xiaohua Feng +3
With the emerging application of Federated Learning (FL) in decision-making scenarios, it is imperative to regulate model fairness to prevent disparities across sensitive groups (e…
A Neuro-inspired Interpretation of Unlearning in Large Language Models through Sample-level Unlearning Difficulty
Xiaohua Feng, Yuyuan Li, Chengye Wang +3
Driven by privacy protection laws and regulations, unlearning in Large Language Models (LLMs) is gaining increasing attention. However, current research often neglects the interpre…
Leveraging Machine Unlearning for Cost-Efficient Preference Alignment
Xiaohua Feng, Yuyuan Li, Huwei Ji +4
Despite advances in Preference Alignment (PA) for Large Language Models (LLMs), mainstream methods like reinforcement learning with human feedback face notable challenges. These ap…
WassFFed: Wasserstein Fair Federated Learning
Zhongxuan Han, Li Zhang, Chaochao Chen +4
Federated Learning (FL) employs a training approach to address scenarios where users' data cannot be shared across clients. Achieving fairness in FL is imperative since training da…
Controllable Unlearning for Image-to-Image Generative Models via -Constrained Optimization
Xiaohua Feng, Yuyuan Li, Chaochao Chen +4
While generative models have made significant advancements in recent years, they also raise concerns such as privacy breaches and biases. Machine unlearning has emerged as a viable…
Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
Yuyuan Li, Chaochao Chen, Xiaolin Zheng +4
With the growing privacy concerns in recommender systems, recommendation unlearning, i.e., forgetting the impact of specific learned targets, is getting increasing attention. Exist…
Class-wise Federated Unlearning: Harnessing Active Forgetting with Teacher-Student Memory Generation
Yuyuan Li, Jiaming Zhang, Yixiu Liu +1
Privacy concerns associated with machine learning models have driven research into machine unlearning, which aims to erase the memory of specific target training data from already…
Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling
Mengying Zhu, Xiaolin Zheng, Yan Wang +2
As the cornerstone of modern portfolio theory, Markowitz's mean-variance optimization is considered a major model adopted in portfolio management. However, due to the difficulty of…