56 citations · 91 across the 32 of their papers we have counts for
19 papers · 1 filter
Fine-Grained Analysis of Catastrophic Forgetting in Continual Learning via Algorithmic Stability
Guodong Zheng, Yan Sun, Peng Wang +5
Understanding catastrophic forgetting, in which learning new tasks degrades performance on previously learned tasks, remains a fundamental challenge in continual learning (CL). Exi…
LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning
Hao Jiang, Enneng Yang, Guojie Zhu +7
Continual learning capability is critical for Industrial LLMs, as deployed models must be continuously updated to meet evolving requirements and environments, rather than repeatedl…
ECHO: Entropy-Confidence Hybrid Optimization for Test-Time Reinforcement Learning
Chu Zhao, Enneng Yang, Yuting Liu +2
Test-time reinforcement learning generates multiple candidate answers via repeated rollouts and performs online updates using pseudo-labels constructed by majority voting. To reduc…
Model Unmerging: Making Your Models Unmergeable for Secure Model Sharing
Zihao Wang, Enneng Yang, Lu Yin +2
Model merging leverages multiple finetuned expert models to construct a multi-task model with low cost, and is gaining increasing attention. However, as a growing number of finetun…
Causal Negative Sampling via Diffusion Model for Out-of-Distribution Recommendation
Chu Zhao, Eneng Yang, Yizhou Dang +3
Heuristic negative sampling enhances recommendation performance by selecting negative samples of varying hardness levels from predefined candidate pools to guide the model toward l…
Distributionally Robust Graph Out-of-Distribution Recommendation via Diffusion Model
Chu Zhao, Enneng Yang, Yuliang Liang +3
The distributionally robust optimization (DRO)-based graph neural network methods improve recommendation systems' out-of-distribution (OOD) generalization by optimizing the model's…
Merging Models on the Fly Without Retraining: A Sequential Approach to Scalable Continual Model Merging
Anke Tang, Enneng Yang, Li Shen +4
Deep model merging represents an emerging research direction that combines multiple fine-tuned models to harness their specialized capabilities across different tasks and domains.…
Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging
Li Shen, Anke Tang, Enneng Yang +6
Multi-task learning (MTL) leverages a shared model to accomplish multiple tasks and facilitate knowledge transfer. Recent research on task arithmetic-based MTL demonstrates that me…
SurgeryV2: Bridging the Gap Between Model Merging and Multi-Task Learning with Deep Representation Surgery
Enneng Yang, Li Shen, Zhenyi Wang +5
Model merging-based multitask learning (MTL) offers a promising approach for performing MTL by merging multiple expert models without requiring access to raw training data. However…
Symmetric Graph Contrastive Learning against Noisy Views for Recommendation
Chu Zhao, Enneng Yang, Yuliang Liang +3
Graph Contrastive Learning (GCL) leverages data augmentation techniques to produce contrasting views, enhancing the accuracy of recommendation systems through learning the consiste…
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
Enneng Yang, Li Shen, Guibing Guo +4
Model merging is an efficient empowerment technique in the machine learning community that does not require the collection of raw training data and does not require expensive compu…
Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation
Chu Zhao, Enneng Yang, Yuliang Liang +5
Graph Neural Networks (GNNs)-based recommendation algorithms typically assume that training and testing data are drawn from independent and identically distributed (IID) spaces. Ho…
FusionBench: A Unified Library and Comprehensive Benchmark for Deep Model Fusion
Anke Tang, Li Shen, Yong Luo +5
Deep model fusion is an emerging technique that unifies the predictions or parameters of several deep neural networks into a single better-performing model in a cost-effective and…
Representation Surgery for Multi-Task Model Merging
Enneng Yang, Li Shen, Zhenyi Wang +4
Multi-task learning (MTL) compresses the information from multiple tasks into a unified backbone to improve computational efficiency and generalization. Recent work directly merges…
AdaMerging: Adaptive Model Merging for Multi-Task Learning
Enneng Yang, Zhenyi Wang, Li Shen +4
Multi-task learning (MTL) aims to empower a model to tackle multiple tasks simultaneously. A recent development known as task arithmetic has revealed that several models, each fine…
Continual Learning From a Stream of APIs
Enneng Yang, Zhenyi Wang, Li Shen +5
Continual learning (CL) aims to learn new tasks without forgetting previous tasks. However, existing CL methods require a large amount of raw data, which is often unavailable due t…
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…
Generalized Embedding Machines for Recommender Systems
Enneng Yang, Xin Xin, Li Shen +1
Factorization machine (FM) is an effective model for feature-based recommendation which utilizes inner product to capture second-order feature interactions. However, one of the maj…