2 citations · 3 across the 12 of their papers we have counts for
14 papers
Federation over Harness
Dixi Yao, Tahseen Rabbani, Tian Li
Modern LLM-powered agents increasingly improve through experience sharing, where distributed agents distill local reasoning experience into reusable knowledge, such as a shared ins…
Persistent Watermarking of Text-to-Image Models
Dixi Yao, Kaiwen Chen, Tahseen Rabbani +1
Text-to-image (T2I) generation is gaining increasing popularity with the general public, motivating the development of reliable mechanisms for copyrighting such models given their…
Federation over Text: Insight Sharing for Multi-Agent Reasoning
Dixi Yao, Tahseen Rabbani, Manzil Zaheer +1
Modern agents specialize in varying domains while there is no clear approach combining different domain skills. We propose a federated learning-like framework, Federation over Text…
Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems
Zongwei Wang, Min Gao, Hongzhi Yin +5
Large language model-empowered agentic recommender systems (ARS) reformulate recommendation as a multi-turn interaction between a recommender agent and a user agent, enabling itera…
ErrorEraser: Unlearning Data Bias for Improved Continual Learning
Xuemei Cao, Hanlin Gu, Xin Yang +4
Continual Learning (CL) primarily aims to retain knowledge to prevent catastrophic forgetting and transfer knowledge to facilitate learning new tasks. Unlike traditional methods, w…
A Survey of Continual Reinforcement Learning
Chaofan Pan, Xin Yang, Yanhua Li +4
Reinforcement Learning (RL) is an important machine learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in this field…
Order-Robust Class Incremental Learning: Graph-Driven Dynamic Similarity Grouping
Guannan Lai, Yujie Li, Xiangkun Wang +3
Class Incremental Learning (CIL) aims to enable models to learn new classes sequentially while retaining knowledge of previous ones. Although current methods have alleviated catast…
Exploring Open-world Continual Learning with Knowns-Unknowns Knowledge Transfer
Yujie Li, Guannan Lai, Xin Yang +3
Open-World Continual Learning (OWCL) is a challenging paradigm where models must incrementally learn new knowledge without forgetting while operating under an open-world assumption…
Improving Open-world Continual Learning under the Constraints of Scarce Labeled Data
Yujie Li, Xiangkun Wang, Xin Yang +3
Open-world continual learning (OWCL) adapts to sequential tasks with open samples, learning knowledge incrementally while preventing forgetting. However, existing OWCL still requir…
A New Perspective on Privacy Protection in Federated Learning with Granular-Ball Computing
Guannan Lai, Yihui Feng, Xin Yang +5
Federated Learning (FL) facilitates collaborative model training while prioritizing privacy by avoiding direct data sharing. However, most existing articles attempt to address chal…
Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundary
Yanhua Li, Xiaocao Ouyang, Chaofan Pan +6
Open intent classification is critical for the development of dialogue systems, aiming to accurately classify known intents into their corresponding classes while identifying unkno…
Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor
Hao Yu, Xin Yang, Le Zhang +4
Federated continual learning (FCL) allows each client to continually update its knowledge from task streams, enhancing the applicability of federated learning in real-world scenari…
FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer
Xin Gao, Xin Yang, Hao Yu +2
Federated Class-Incremental Learning (FCIL) focuses on continually transferring the previous knowledge to learn new classes in dynamic Federated Learning (FL). However, existing me…
DEED: Dynamic Early Exit on Decoder for Accelerating Encoder-Decoder Transformer Models
Peng Tang, Pengkai Zhu, Tian Li +3
Encoder-decoder transformer models have achieved great success on various vision-language (VL) tasks, but they suffer from high inference latency. Typically, the decoder takes up m…