56 citations · 76 across the 8 of their papers we have counts for
10 papers
Formalizing Forgetting: Theory of Forgetting for Online Continual Learning
Renjie Cao, Yifei Cheng, Li Shen
Online continual learning (OCL) requires a learner to acquire a sequence of tasks in a single pass over the data, and catastrophic forgetting of earlier tasks remains its central c…
Adaptive Defense against Harmful Fine-Tuning for Large Language Models via Bayesian Data Scheduler
Zixuan Hu, Li Shen, Zhenyi Wang +2
Harmful fine-tuning poses critical safety risks to fine-tuning-as-a-service for large language models. Existing defense strategies preemptively build robustness via attack simulati…
Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models
Yongxian Wei, Zixuan Hu, Li Shen +4
Data-Free Meta-Learning (DFML) aims to derive knowledge from a collection of pre-trained models without accessing their original data, enabling the rapid adaptation to new unseen t…
FREE: Faster and Better Data-Free Meta-Learning
Yongxian Wei, Zixuan Hu, Zhenyi Wang +3
Data-Free Meta-Learning (DFML) aims to extract knowledge from a collection of pre-trained models without requiring the original data, presenting practical benefits in contexts cons…
A Unified and General Framework for Continual Learning
Zhenyi Wang, Yan Li, Li Shen +1
Continual Learning (CL) focuses on learning from dynamic and changing data distributions while retaining previously acquired knowledge. Various methods have been developed to addre…
Task-Distributionally Robust Data-Free Meta-Learning
Zixuan Hu, Yongxian Wei, Li Shen +4
Data-Free Meta-Learning (DFML) aims to enable efficient learning of unseen few-shot tasks, by meta-learning from multiple pre-trained models without accessing their original traini…
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning
Zhenyi Wang, Enneng Yang, Li Shen +1
Forgetting refers to the loss or deterioration of previously acquired knowledge. While existing surveys on forgetting have primarily focused on continual learning, forgetting is a…
AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning
Enneng Yang, Junwei Pan, Ximei Wang +6
Multi-task learning (MTL) models have demonstrated impressive results in computer vision, natural language processing, and recommender systems. Even though many approaches have bee…
Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions
Zhenyi Wang, Li Shen, Le Fang +4
The paradigm of machine intelligence moves from purely supervised learning to a more practical scenario when many loosely related unlabeled data are available and labeled data is s…
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution
Zhenyi Wang, Li Shen, Le Fang +3
Task-free continual learning (CL) aims to learn a non-stationary data stream without explicit task definitions and not forget previous knowledge. The widely adopted memory replay a…