6 papers · 1 filter
What Is Preference Optimization Doing, and Why?
Yue Wang, Qizhou Wang, Zizhuo Zhang +3
Preference optimization (PO) is indispensable for large language models (LLMs), with methods such as direct preference optimization (DPO) and proximal policy optimization (PPO) ach…
Learning without Isolation: Pathway Protection for Continual Learning
Zhikang Chen, Abudukelimu Wuerkaixi, Sen Cui +10
Deep networks are prone to catastrophic forgetting during sequential task learning, i.e., losing the knowledge about old tasks upon learning new tasks. To this end, continual learn…
Towards Effective Evaluations and Comparisons for LLM Unlearning Methods
Qizhou Wang, Bo Han, Puning Yang +3
The imperative to eliminate undesirable data memorization underscores the significance of machine unlearning for large language models (LLMs). Recent research has introduced a seri…
Accurate Forgetting for Heterogeneous Federated Continual Learning
Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang +6
Recent years have witnessed a burgeoning interest in federated learning (FL). However, the contexts in which clients engage in sequential learning remain under-explored. Bridging F…
Decoupling the Class Label and the Target Concept in Machine Unlearning
Jianing Zhu, Bo Han, Jiangchao Yao +3
Machine unlearning as an emerging research topic for data regulations, aims to adjust a trained model to approximate a retrained one that excludes a portion of training data. Previ…
Balancing Similarity and Complementarity for Federated Learning
Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi +5
In mobile and IoT systems, Federated Learning (FL) is increasingly important for effectively using data while maintaining user privacy. One key challenge in FL is managing statisti…